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School of Interactive Computing

College of computing.

Ph.D. Programs

For those students looking to build a career in computing research, the School of Interactive Computing offers a range of Ph.D. programs that allow students to work side-by-side with some of the most brilliant researchers and computer scientists in the world. For those looking to join the ranks of academia, we regularly place our doctorate graduates in tenure-track positions in top programs. The School maintains strong research relationships with companies from Fortune 500 companies to the latest startups that allow graduates to continue their research in jobs at some of the world’s hottest private sector employers.

If you’re ready to join the ranks of the world’s top computer scientists, we’ve got a Ph.D. program that’s ready to challenge you.

Ph.D. IN COMPUTER SCIENCE

As a research-oriented degree, the Ph.D. in Computer Science prepares exceptional students for careers at the cutting edge of academia, industry and government. 

Ph.D. IN HUMAN-CENTERED COMPUTING

A program devoted to the interdisciplinary science of designing computational artifacts that better support human endeavors.

Ph.D. IN ROBOTICS

Educating a new generation of robotics researchers and preparing them to be impactful contributors upon entering the high-tech workforce.

Ph.D. IN MACHINE LEARNING

Machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.

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Best Online Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salary

Machine learning is a rapidly growing, fascinating field dealing with algorithm development that can be used to make predictions from data. The best online PhD in Machine Learning prepares students for a career in this promising field.

The best online doctorates in machine learning offer students a comprehensive education in all aspects of the field. Students are also provided with the opportunity to choose a specialization such as deep learning, natural language processing , or computer vision. Find out in this article what machine learning PhD online degree program best fits you and the machine learning jobs for graduates.

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Can you get a phd in machine learning online.

Yes, you can get a PhD in Machine Learning online. The online learning system has seen rapid growth in many academic fields and has given students the opportunity to virtually access the academic curriculum remotely.

Many online PhD programs in the United States are accredited and designed with working professionals in mind. Online learning is a great way to earn a doctorate without sacrificing your day job, and in most cases, online students can complete their entire academic journey without stepping foot on campus.

Is an Online PhD Respected?

Yes, an online PhD is respected when it is obtained from an accredited institution in the US. A PhD from an unaccredited school is regarded as just an expensive piece of paper by many other academic institutions.

In regard to employment, many companies and organizations respect an online PhD, holding it to the same standard as an in-person PhD. However, some employers prefer in-person degrees and will disregard online degrees. Ensure your potential future employer accepts online degrees as credible education.

What Is the Best Online PhD Program in Machine Learning?

The best online PhD program in machine learning is at Clarkson University in Potsdam, New York. It is regionally accredited by the Middle States Commission on Higher Education and has an excellent reputation within the academic community, a student-to-faculty ratio of 12 to one, and one in five of its 44,000 alumni is a CEO or executive.

Why Clarkson University Has the Best Online PhD Program in Machine Learning

Clarkson University has the best machine learning PhD program not only because it is accredited by the Middle States Commission on Higher Education (MSCHE) but also because of its US News & World Report ranking. MSCHE is a regionally recognized accreditation association that uses a rigorous and comprehensive system for the purpose of accreditation.

Referring to US News & World Report, Clarkson University is ranked 127 for best national universities out of 4000 degree-granting academic institutions in the United States and 49 for best value schools.

Best Online Master’s Degrees

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Online PhD in Machine Learning Admission Requirements

The admission requirements for an online PhD in Machine Learning typically include a master’s degree or Bachelor’s in Machine Learning or a related subject like the field of engineering. Moreover, prepare to submit official transcripts from previously attended postsecondary institutions and GRE test scores.

Additionally, you may be asked to submit three letters of recommendation, a statement of purpose, a CV or resume, and prove your knowledge of calculus and your fluency in computer programming languages like Python and Java. Below is a list of the typical admission requirements needed by distinct schools that offer a machine learning PhD program.

• Master’s or bachelor’s degree in a relevant field
• Official transcripts and GRE test scores
• Letters of recommendation
• Statement of purpose
• CV or resume
• Knowledge of programming and calculus

Best Online PhDs in Machine Learning: Top Degree Program Details

Best online phds in machine learning: top university programs to get a phd in machine learning online.

The top university programs to get a PhD in Machine Learning are at Clarkson University, Aspen University, Capitol Technology University, The University of Rhode Island, and The University of the Cumberlands, among other distinct schools.

This section discusses the properties, requirements, and descriptions of the best universities offering online PhD in Machine Learning programs. We have created this list below to narrow down your school search for these graduate-level in-depth study programs.

Aspen University is a Distance Education Accrediting Commission accredited university. It was established in 1987 as a private for-profit online university offering undergraduate and graduate degrees in computer science, business information systems, and project management.

Aspen University in Phoenix, Arizona is a known member of the Council for Adult and Experiential Learning and is dedicated to supporting adult learners in achieving a professional career in whatever field they desire.

DSc in Computer Science

This doctoral degree teaches students the theory and practical application of computer science in data science, application design, and computer architecture. It contains 20 courses, including artificial intelligence, risk analysis, and system metrics. 

These courses are offered online and aim to impart students with the necessary skills for improving existing technology, as well as evaluating and applying them. It also contains courses that aid doctoral students in carrying out their research dissertations.

DSc in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 5 years and 7 months
• Acceptance Rate: N/A
• Tuition and Fees: $375/month

DSc in Computer Science Admission Requirements

• Master’s degree
• Statement of goals
• Minimum of 3.0 GPA
• Must know about object-oriented development

Capitol Technology University was founded in 1927 and offers online programs at the undergraduate, graduate, and doctoral levels. The areas of study in which these online programs are offered include business, technology, and the field of engineering.

PhD in Artificial Intelligence

This is a research-based PhD program that offers students the opportunity to conduct research in any field of their choice. Throughout the program, student work must be approved by the academic supervisor. Students are to submit a thesis and give an oral presentation which will be supervised by an expert in the field.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 2 to 3 years
• Tuition and Fees: $933/credit

PhD in Artificial Intelligence Admission Requirements

• Application fee of $100
• Master’s degree in a relevant field
• Minimum of five years of related work experience
• Two recommendation letters

Founded in 1973, City University of Seattle is recognized as a top 10 educator of adults nationwide, as ranked by the US News & World Report for school rankings. It offers online undergraduate, graduate, and doctoral programs designed for working professionals

PhD in Information Technology

The program’s curriculum consists of courses in machine and deep learning. Candidates are given the option to propose their depth of study, which requires approval from the academic director. The program consists of core courses, concentration courses, a comprehensive examination, a research core, and a dissertation. 

PhD in Information Technology Overview

• Accreditation: Northwest Commission on Colleges and Universities
• Program Length: Flexible
• Acceptance Rate: 100% due to open admission policy
• Tuition and Fees: $765/credit

PhD in Information Technology Admission Requirements

• A master’s degree from an accredited or recognized institution
• CV and resume, and three references letters 
• Proof of English proficiency
• Interview with admissions advisor
• State goals related to your academic work

Founded in 1896 to honor Thomas S. Clarkson, Clarkson University offers flexible online degree programs at the undergraduate and graduate levels. It is a research university that leads in technology education. 

PhD in Computer Science

This doctoral program places emphasis on areas such as artificial intelligence , software, security, and networking. Current students are required to complete 36 credits of computer science foundation and research-oriented courses, elective courses, achieve candidacy within the first two years of the program, and propose and defend a thesis.

PhD in Computer Science Overview

• Program Length: 3 years
• Tuition and Fees: $1,533/credit

PhD in Computer Science Admission Requirements

• Complete the online application form
• Resume, statement of purpose, and three letters of recommendation
• English proficiency test for international applicants (TOEFL, IELTS, PTE, and Duolingo English Test)

Northcentral University is a private university established in 1996 and is designed for flexibility by offering programs of distance learning for working professionals. It practices a distinctive one-to-one learning system and has a dedicated doctoral faculty.

In this doctorate program, besides writing papers about past research, students are allowed to propose their research. Its curriculum consists of subjects such as software engineering , artificial intelligence, data mining, and cyber security. Through the course, students conduct research and examine real-world issues in the field of computer science.

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• Accreditation: WASC Senior College and University Commission
• Program Length: 3 years and 4 months
• Tuition and Fees: $1,094/credit
• Master’s degree from an accredited institution
• Official transcripts
• English proficiency exam score for international students

Nova Southeastern University was founded in 1964 in Fort Lauderdale, Florida. It offers online graduate and undergraduate courses and conducts a wide variety of interdisciplinary healthcare research. It is home to national athletics champions and Olympians.

This program provides research in computer science. Its format of learning combines both traditional and online instruction designed with consideration for working professionals . Its coursework consists of research in computer science areas, including cyber security, software engineering, and artificial intelligence.

• Accreditation: Southern Association of Colleges and Schools, Commission on Colleges
• Program Length: Not specified
• Tuition and Fees: $1,282/credit
• Online application and $50 application fee
• A bachelor’s or master’s degree in a relevant field from a regionally accredited institution
• GPA of at least 3.20 
• Official transcripts from all institutions attended 
• A resume  
• Essay, and three letters of recommendation

The University of North Dakota was founded in 1883, six years before North Dakota was made a state. Today, it offers several academic programs in undergraduate, graduate, and doctoral fields and is known for conducting research in areas that include medicine, aerospace, and engineering.

This PhD in Computer Science curriculum consists of courses in machine learning, software engineering, applications of AI, computer forensics, and computer networks which benefit students by granting them proficiencies in areas such as artificial intelligence, compiler design, operating systems, simulation, databases, and networks.

• Accreditation: Higher Learning Commission
• Program Length: 4 to 5 years
• Tuition and Fees: $545.16/credit (in state); $817.73/ credit (out of state)
• Application fee of $35
• Master’s or bachelor’s degree in engineering or a related science field
• GPA of 3.0 on a 4.0 scale and GRE test score
• Official copy of all college and university academic transcripts
• Statement of academic goals and three letters of recommendation
• Expertise in a high-level programming language and basic knowledge of data structures, formal languages, computer architecture and OS, calculus, statistics, and linear algebra 
• English language proficiency

The University of Rhode Island is a public research institution founded in 1892. It conducts extensive research in the field of science. It offers online, on-site, and hybrid programs at the graduate and undergraduate levels, as well as certificate programs.

In this PhD in Computer Science program, students are involved in research geared toward producing new intellectual and innovative contributions to the field of computer science. It offers a combination of on-campus, online, and day and evening classes. It consists of courses in machine learning, artificial intelligence, software engineering, and systems simulation.

• Accreditation: New England Commission of Higher Education
• Program Length: 4 years
• Tuition and Fees: $14,454/year (in-state); $27,906/ year (out of state)
• An undergraduate degree from a regionally accredited institution in the US
• A minimum GPA of 3.0
• All official college transcripts
• Personal statement
• An application fee of $65

Founded in 1888 by Baptist ministers in Williamsburg KY, today the University of the Cumberlands offers online master's and doctoral degree programs in the fields of education, information technology, and business.

The program requires 18 credit hours of core courses which include information technology geared toward creating machine learning engineers . Its curriculum focuses on predictive analytics and other skills students need to become experts in cyber crime security, big data, and smart technologies.

Students have the option to specialize in information systems security, information technology, digital forensics, or blockchain technologies. Students will complete 21 credit hours of professional research while working toward a dissertation.

• Tuition and Fees: $500/credit
• A master’s degree from a regionally accredited institution
• TOEFL for non-native English speakers
• Application fee of $30

Wright State University was first seen in 1964 as a branch campus for Ohio State University and Miami University. It is a Carnegie classified research university and offers research at the undergraduate, graduate, and doctoral levels.

PhD in Computer Science and Engineering

This degree is awarded to students who show excellence in study and research that significantly contributes to the field of computer science and engineering. The degree requirements include an A grade completion of the core coursework in two areas and at least a B in the third. 

Students are to complete a minimum of 18 hours of residency research before taking the candidacy exam, which must be completed with a satisfactory grade. Also, a minimum of 12 hours of dissertation research is needed before the dissertation defense, which has to be approved.

PhD in Computer Science and Engineering Overview

• Program Length: 10 years time limit
• Tuition and Fees: $660/credit (in state); $1,125/ credit (out of state)
• Bachelor’s or master’s degree in a related discipline (computer science or engineering)
• Minimum GPA of 3.0 if admitted with a bachelor’s degree or 3.3 with a master’s degree
• GRE general test portion
• TOEFL score for non-native English speakers
• Knowledge of high-level programming languages, computer organization, operating systems, data structures, and computer systems design
• A record that indicates potential for a career in research

Online Machine Learning PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Machine Learning?

It is very hard to complete an online PhD in Machine Learning. According to a paper published in the International Journal of Doctoral Studies, there is a PhD attrition rate of 50 percent in the US within the past 50 years. Therefore, the graduation rate for doctorate students is approximately 50 percent.

How Long Does It Take to Get a PhD in Machine Learning Online?

It takes about four years to get a PhD in Machine Learning online, which is fast when compared to a traditional in-person PhD program which may take over seven years to complete. Online PhD programs are accelerated by default, so the curriculum focuses on the major needs of a PhD graduate in the areas of research, thesis, and dissertation.

Students may be able to reduce the time spent pursuing a PhD in Machine Learning by first acquiring a master’s degree in the field. If you choose to pursue a PhD on a part-time schedule as opposed to full-time study, it will significantly increase the time it takes to acquire the degree.

How Hard Is an Online Doctorate in Machine Learning?

Getting an online doctorate in machine learning is very hard, as are most graduate programs. Besides the rigorous research, strict requirements, deadlines, qualification examinations, and dissertations, other challenges may exist, such as limited student connection with the faculty members, isolation, financial issues, and lack of an adequate work-life balance .

Getting a doctorate in any field is not easy. In fact, there is research to suggest that online doctorate students face challenges regarding culture and academia. As a result of these challenges, many students drop out from their PhD programs.

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What Courses Are in an Online Machine Learning PhD Program?

The courses in an online machine learning PhD program include an introduction to machine learning and deep learning, artificial intelligence, statistical theories, data mining , system simulation, computer programming, and software development.

Main Areas of Study in a Machine Learning PhD Program

• Machine learning
• Deep learning
• Artificial intelligence
• Databases and data mining
• Statistical theory
• Software engineering
• Systems simulation

How Much Does Getting an Online Machine Learning PhD Cost?

On average, it costs $19,314 per year to get a PhD in Machine Learning, according to the National Center of Education Statistics (NCES). However, this figure is not fixed, as the total tuition for a PhD program varies from school to school.

Private institutions generally cost more than public institutions, but there are funding opportunities for PhD students. Some PhD programs may guarantee financial aid for all their students regardless of merit.

How to Pay for an Online PhD Program in Machine Learning

You can pay for an online PhD in Machine Learning by taking advantage of student loans, scholarships, grants, teaching and research assistantships, graduate assistantships, and fellowship assistantships. As a result, most PhD students spend less than the tuition fee displayed on a school’s website.

How to Get an Online PhD for Free

You cannot get an online PhD in Machine Learning for free. However, there are ways to reduce the cost, or get partial tuition discounts and stipends through graduate assistantships, fellowships, scholarships, or grants.

What Is the Most Affordable Online PhD in Machine Learning Degree Program?

The most affordable online PhD in Machine Learning based on cost per credit is at Aspen University in Phoenix, Arizona. It charges $375 per month, which, when multiplied by the 67 months it takes to complete the program, results in a total of $25,125 for the entire program. This is more affordable compared to a school like Clarkson University, which charges $1,533 per credit hour.

Most Affordable Online PhD Programs in Machine Learning: In Brief

Why you should get an online phd in machine learning.

You should get an online PhD in Machine Learning because having a PhD offers you a stronger advantage in terms of employability, salary, and in your career in general that would otherwise be unavailable with just a bachelor’s and master’s degree.

Top Reasons for Getting a PhD in Machine Learning

• Research opportunities. PhD students get the opportunity to be involved in rigorous and innovative research that may positively impact humanity, add to the world’s knowledge, and improve the lives of others.
• Expertise development. A PhD is the highest level of academic degree, and as a result, PhD holders have expert-level knowledge in whichever field they acquire a PhD in. However, it is advised to only get a PhD if you are very interested in the field and willing to explore your interest and expand your understanding through cutting-edge research.
• Access to better jobs. There are lots of bachelor’s and master’s degree graduates in the job market, and earning a PhD will help you stick out from the crowd. A PhD reveals career opportunities that may not be available to bachelor’s and master’s degree grads.
• Networking opportunities . During a PhD program, students are in contact with top lecturers and academic experts by attending guest lectures, conferences, seminars, and workshops. Students can network with colleagues and classmates, which helps put them in a good position after their academic journey.

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What Is the Difference Between an On-Campus Machine Learning PhD and an Online PhD in Machine Learning?

The difference between an on-campus machine learning PhD and an online PhD in Machine Learning is primarily the mode of learning. Online PhDs are as rigorous and effective as their on-campus counterparts.

However, there may be some slight differences between the two in terms of cost, schedule, quality, and funding. Some of the differences that may exist are discussed below.

Online PhD vs On-Campus PhD: Key Differences

• Affordability. An online PhD is more affordable compared to the traditional on-campus alternative. An on-campus PhD can cost as much as $30,000 per year, while an online PhD may be as low as $20,000 per year.
• Flexibility. Online PhD students have the liberty to conduct in-depth study and research at their own time as opposed to the schedule of an in-person PhD program. Moreover, most online PhD programs don’t have an enrollment date, and some online PhD work is asynchronous, meaning students can take classes from anywhere at their convenience.
• Quality. Traditionally acquired PhDs are thought to be superior to their online counterparts by some employers and academics, probably due to sentiment. However, the quality of an online PhD is dependent on the research subject, the school’s reputation, and accreditation.
• Availability of funding. Funding available for online PhD programs may be limited due to some geographical constraints. For example, online PhD students cannot take up teaching assistantship positions unless they are willing to be physically present.

How to Get a PhD in Machine Learning Online: A Step-by-Step Guide

To get a PhD in Machine Learning, you need to first apply online to a PhD program. If accepted, you must enroll in the required classes and complete the academic coursework, research, and a series of academic milestones, which include attaining candidacy, passing the qualification examinations, proposing, writing, and defending your dissertation.

To begin your journey to acquiring a PhD in Machine Learning, you first need to apply online to the school of your choice. You also need to fulfill the admission requirements, including possessing a master's or bachelor's degree–depending on the school–in a relevant field, a minimum grade point average, letters of recommendation, and GRE test scores . 

Many online PhD programs require students to take and pass a minimum number of credit hours in core and elective courses. A typical online PhD in Machine Learning program consists of about 70 to 90 credit hours that involve intensive research in a provided or chosen area of concentration. 

Obtaining a PhD in Machine Learning allows an individual to become a world-renowned expert in the field. After completing a rigorous course of study and passing a series of exams, the doctoral candidate would then undertake an original research project that contributes new knowledge to the field. Upon successful completion of the degree, the graduate would be able to pursue a career in academia or industry. 

Examinations are an essential part of any education. They test a student's understanding of the material and help them to learn and remember the information. If you want to earn a machine learning PhD, you must pass the examinations for various core and required courses. Then, you will need to complete and defend your dissertation.

A dissertation is a research paper that is submitted to and defended by a graduate student to earn a graduate degree. To graduate with a PhD in Machine Learning, you are required to write a dissertation on a topic related to machine learning. Your doctoral dissertation must demonstrate your knowledge and understanding of the field of machine learning, as well as your ability to conduct original research in the field.

Online PhD in Machine Learning Salary and Job Outlook

The job outlook for machine learning jobs is 22 percent between 2020 and 2030 , with the number of new jobs expected in this time frame being 7,200, according to the US Bureau of Labor Statistics. The average salary for computer and information research scientists, which is a category that machine learning professionals belong to, is $131,490 per year .

What Can You Do With an Online Doctorate in Machine Learning?

With an online doctorate in machine learning, you can qualify for specialization roles and lead machine learning positions, including senior machine learning engineer and computer research scientist.

Depending on your preferences, you may also opt for a research and academic career path to become a university professor. The list below is a list of the best jobs for PhD in Machine Learning graduates.

Best Jobs with a PhD in Machine Learning

• Senior Machine Learning Engineer
• Computer and Information Research Scientist
• Data Scientist
• Software Engineer
• Postsecondary Teacher

Potential Careers With a Machine Learning Degree

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What Is the Average Salary for an Online PhD Holder in Machine Learning? 

The average salary for a PhD in Machine Learning holder is $108,000 per year , according to PayScale’s salary for skills in machine learning. The average salary a PhD holder receives depends on the location and position you apply for.

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs for online phd holders.

The best machine learning jobs for online PhD holders are typically high-paying jobs that require advanced-level skills that coincide with the nature of the position they undertake. Below are some typical job titles that online machine learning PhD degree holders assume.

A senior machine learning engineer oversees a team of machine engineers charged with designing and developing effective machine learning and deep learning solutions implemented in machine learning systems.

• Salary with a Machine Learning PhD: $153,255
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas

Computer and information research scientists research and develop new ways of solving complex computing problems and apply existing technology. They work to significantly increase the knowledge in the field of computer science, which will aid in the production of more efficient software and hardware technologies.

• Salary with a Machine Learning PhD: $131,490

A senior data scientist is responsible for developing data mining and machine learning techniques to solve complex business problems. They identify patterns and trends in large data sets, develop models to improve forecasting and decision making, and effectively communicate data-driven insights to non-technical stakeholders and lead a team of data analysts.

• Salary with a Machine Learning PhD: $127,455

A software engineer is a professional that develops and maintains software. They work on a variety of software, from operating systems to video games, and may be involved in the development of websites. They must also have an excellent understanding of computer programming languages and be able to solve complex problems.

• Salary with a Machine Learning PhD: $121,115
• Number of Jobs: 1,847,900
• Highest-Paying States: Washington, California, New York

Postsecondary teachers are in charge of lecturing students in colleges and universities. They are also responsible for instructing adults in several academic and non-academic subjects including career, work, and research.

• Salary with a Machine Learning PhD: $79,640
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: California, Oregon, District of Columbia

Is It Worth It to Do a PhD in Machine Learning Online?

Yes, it is worth it to do a PhD in Machine Learning online. Getting a PhD is not for everyone, as the process will require tremendous effort and discipline, but it can be rewarding. A PhD in Machine Learning online allows you to learn from some of the best minds in the field.

You can also specialize in an area of your choice, such as big data, natural language processing, or deep learning. Specializing in one area for your PhD in Machine Learning allows you to deep-dive into that subject and build doctorate-level expertise.

An online PhD in Machine Learning provides students with the same high-quality education as a traditional PhD but with more flexibility and affordability. You’ll have access to top-notch instructors, state-of-the-art technology, and a thriving online community of experts.

Additional Reading About Machine Learning

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Online PhD in Machine Learning FAQ

Yes, you should get an online PhD in Machine Learning if it is critical for your career prospects. An online PhD in Machine Learning allows you to learn at your own pace and keep your day job while you pursue your degree. In the end, it sets you up for the highest-earning jobs in the machine learning industry , with better pay and a larger professional network.

The type of research you will carry out as a machine learning student includes research in deep learning, neural networks , machine learning algorithms, supervised and unsupervised machine learning, predictive learning, and computer vision. Students will make use of quantitative and experimental methods of research as well as the use of optimal feature selection.

You can choose a concentration for an online machine learning PhD by factoring in your interests, strengths, and career goals. You may also consider recent trends, the average salary of machine learning professionals , or the career options the machine learning industry has to offer when choosing a machine learning concentration.

Examples of online machine learning PhD dissertations include experimental quantum speed-up in reinforcement learning agents, improving automated medical diagnosis systems with machine learning technologies, regulating deep learning and robotics, and the use of machines and robotics in medical procedures.

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Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

PhD Admissions

Main navigation.

The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

Eligibility

To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

Application Checklist

An completed online application must be submitted by the CS Department application deadline and can be found  here .

Application Deadlines

The online application can be found here  and we will only one admissions cycle for the PhD program per respective academic term.

Department of Computer Science

University | A to Z | Departments

Computer Science

• Postgraduate study
• Research Degrees

PhD in Computer Science

You will be based in the Department of Computer Science overlooking the lake on Campus East .

You will benefit from modern offices and collaboration spaces, and well-equipped research labs with a specialist in-department team to support your requirements throughout your studies. 

We will provide you with a laptop connected to the University network, and you will have 24/7 access to your desk and workspace. Distance learning students are allocated a work desk for the duration of their stay while they are in York.

For on-campus researchers, most of your training and supervision meetings will take place on campus at the University of York, though your research may take you further afield.

PhD by distance learning

We offer the opportunity to study for a PhD by distance learning. This is available to students based in the UK and abroad, studying full-time or part-time. Our PhD by distance learning offers the same high quality of supervisory support (primarily online), and demands the same level of academic rigour as a campus-based PhD.

You will undertake your research and thesis production remotely, joining us on campus only occasionally. You will be expected to visit York at your own expense at the following stages of your study:

• Two weeks at the start of enrolment for induction, to meet your supervisor and your research group, and to meet other PhD students;
• Two one-week visits each year at important stages ('milestones') of your study (the number of visits is reduced accordingly if you are a part-time student);
• You will normally attend your PhD viva in person.

When you are not in York, you will continue to benefit from regular supervision meetings using online communication platforms, such as Zoom. Read more about how we support distance learners .

Are you an international applicant? It is important for you to note that it is your responsibility to meet any requirements for legal entry into the UK at the time of each of your visits. While the University and Department can provide supporting letters, the University cannot make any guarantees regarding entry visas or legal residence.  Read more about applying for a visa.

Entry requirements

Undergraduate and masters degrees.

The PhD in Computer Science is intended for students who already have a good first degree in Computer Science or a related field.

For entry to the PhD programme, we require at least a 2:1 undergraduate degree, or a qualification equivalent to a UK Masters degree with a minimum average grade of 60%.

We are willing to consider your application if you do not fit this profile, providing you are able to demonstrate that you have the required amount of Computer Science knowledge and experience to succeed on the programme.

English language requirements

If English is not your first language you must provide evidence of your ability.

Find out more about English Language requirements for research degrees

How to apply

Find a potential supervisor.

You should find a potential supervisor in our Department whose area of research overlaps with yours. We encourage you to contact them to discuss your research proposal before you apply. Please identify the name of your potential supervisor in your application.

On our Research web pages, you can explore our research groups which reflect the core research strengths and expertise within the Department of Computer Science. On the web page for each research group, you'll find more information about the aims and objectives of the group and the names of group members. You can use this information to identify the groups where research interests match your own.

If you have any questions or need further information, please contact [email protected] .

Submit your application

We require you to submit the following documents:

• Research proposal
• Academic transcript(s )
• Your curriculum vitae (CV)
• Personal statement
• Details of two academic referees

Your research proposal needs to outline the nature of your proposed study and give some indication of how you will conduct your research. The purpose of this exercise is to ensure that you and your potential supervisor(s) have matching research interests.

Your proposal can build on your chosen supervisor's area of work and may be prepared with the help of your chosen supervisor. It should be about 500 to 1,000 words in length, in English and in your own words. Read more about writing a research proposal .

You can apply and send all your documentation electronically through our online system. You don’t need to complete your application all at once: you can start it, save it and finish it later.

After you have applied, you can track the status of your application and view any official correspondence online. If you have applied for an advertised scholarship, decisions on funded places may take a little longer.

Applicant interviews

If we are impressed by your full application, personal statement and references, we will invite you to interview.

The interview panel will be made up of your potential supervisor(s) and another independent academic. During your interview, it is important that you demonstrate an understanding of your chosen topic and its supporting theories.

For students based outside the UK, interviews are held online via Zoom. Applicants based in the UK are offered the opportunity to attend their interview in York. If you choose to attend in person, your visit will include a tour of the Department and its facilities.

Related links Explore our PhD opportunities Research groups in the Department of Computer Science About our research degrees Applying for a research degree Funding for research degrees Information for International students Accommodation Life at York

Department of Computer Science Deramore Lane , University of York , Heslington , York , YO10 5GH , UK Tel: work 01904 325501

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• Doing a PhD in Artificial Intelligence

Artificial intelligence and intelligent systems are thought to be the key to the next ‘industrial revolution’. In a data-rich world, developing an artificial intelligence which can learn from its experience and call on human behaviour to make decisions could change the way we live and offer endless economic, social and scientific applications. Nationwide, there is an increasing demand for AI workers, as the world is becoming more reliant on developing technology and automated systems. Consequently, more and more people are pursuing postgraduate research in AI.

What Does a PhD in Artificial Intelligence Focus On?

A PhD in artificial intelligence will give you a deep understanding of AI, allow you to contribute to the development of emerging technology, and equip you with highly applicable technical skills. For example, engineering applications of artificial intelligence include automation of tasks and parametric modelling. Medical applications include using data-science approaches to identify patterns of illness in clinical data. Financial applications include using machine learning platforms to crunch huge amounts of data and help credit lenders in analysing risk and assess potential borrowers.

Artificial Intelligence PhD programme can focus on:

• Machine learning
• Deep learning
• Natural language processing
• Deep neural network architecture
• Human-machine interaction
• Augmented reality
• And countless other areas

Browse PhDs in Artificial Intelligence

Application of artificial intelligence to multiphysics problems in materials design, from text to tech: shaping the future of physics-based simulations with ai-driven generative models, study of the human-vehicle interactions by a high-end dynamic driving simulator, coventry university postgraduate research studentships, discovery of solid state electrolytes using deep learning, entry requirements for a phd in artificial intelligence.

The entry requirements for a PhD in AI are typically an upper second-class honours degree (or international equivalent) in a relevant subject from an accredited university. Subjects considered relevant to artificial intelligence include computer science, engineering, mathematics, statistics, electronics/electrical engineering or science.

Some research courses also require applicants to possess experience in programming, the desired programming language will be specific to the research project. Academic or work experience in machine learning or data science are typically favourable for applications.

International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.

Duration and Programme Types

Like most PhDs, a doctoral programme in Artificial Intelligence typically takes 3-4 years full-time, or 6 years part-time .

Aside from the traditional PhD, there is also the CDT PhD. Many Universities have Centres for Doctoral Training (CDTs) which are often funded by the UKRI centre . These CDTs can offer the CDT PhD which is a specialised PhD programme in artificial intelligence. The main difference between a standard PhD in artificial intelligence and a CDT PhD in artificial intelligence is that the latter includes additional modules which give candidates training in neuroscience, entrepreneurship, high performance computing, AI ethics and science communication.

Due to the different research areas you can pursue within the artificial intelligence field, the nature of programmes can vary. Some PhD research programmes are computational based and heavily reliant on coding, mathematics and lab work. Other research programmes can be people facing, involving questionnaires, for example to determine public perception on proposed legislation.

Costs and Funding

Annual tuition fees for PhDs in Artificial Intelligence are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £25,000 per academic year.

A variety of scholarships and funding support options are available for postgraduate study. For Artificial Intelligence in particular, the UKRI and ESPRC offer a number of studentships and CDT opportunities across varies universities. Many universities have research centres which are partnered with UK research councils and offer fully funded programmes. Funding is generally available for UK/EU students. International students are also eligible for some funding opportunities, but these tend to be less widely available.

Available Career Paths in AI

One of the key advantages of Artificial Intelligence is that it has a wide range of applications, and hence there are many career paths available. As computer systems and data have become more integrated in everyday life, the demand for experts in AI has grown rapidly. This high demand has resulted in many high job security and lucrative salaries.

Examples destinations for an AI PhD student include:

Data Analyst – If you are very analytical research student, you may use your artificial intelligence PhD to pursue a career in data science or analysis. Data analysts can be found in engineering, finance, healthcare, and everywhere in between. They are responsible for data crunching and using their skills to present complex information in a clear manner – visually and orally. Typical duties include record management, maintaining automated processes, monitoring analytics and KPIs, improving algorithms, and creating dashboards for clients. The average salary for data analysts in the UK is around £30,000 – £45,000, though this number can increase drastically depending on the sector.

Cyber Security – As cyber-attacks are becoming more commonplace, industries are looking to develop their cyber security, and salaries are seeing a sharp increase accordingly. AI doctorates are well placed for a career in cyber security, and typical career destinations include security analysts, penetration testers, systems engineers, web developers and cybersecurity consultants. In these roles, you will be responsible for protecting IT infrastructure and help develop security systems.

Machine learning – Often those with a PhD in AI become machine learning engineers, responsible for the development of intelligent systems. Machine learning is a subset of AI which focuses on the idea that machines can be programmed to learn from data and experience to improve decision making without human input. Machine learning is perhaps at the forefront of AI research, and there are numerous programmes look to improve its capabilities. This is well suited for those who enjoy the mathematical and programming side of computer science. Typical duties include managing data pipelines, developing algorithms, liaising with stakeholders, analysing datasets, and leading software design. Entry level salaries are around £35,000 and can exceed £150,000 with experience. Deep learning is similar to machine learning, the main difference is that deep learning aims to create artificial ‘neural networks’.

Postgraduate research often leads to an academic career. As an academic you can propose your own research projects based on your interest and supervise students. As a professor you can shape the next generation of AI experts and as a researcher you can make use of a university’s department resources, facilities and industrial ties to work with cutting edge technology and push the boundaries of our knowledge.

Browse PhDs Now

Join thousands of students.

Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.

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Data & Artificial Intelligence

WHY ENROLL IN THIS PROGRAM?

Get ready for a PhD by starting research at an early stage

Be closely associated with the research activities carried out in a world-renowned innovation cluster

Benefit from individual and personalized supervision by a faculty member

• Description
• Associated laboratories

The Data&AI PhD Track is a 5-year integrated Master’s/PhD program that provides a research-intensive training in the multi-disciplinary field of Data sciences. The program is open to outstanding students from a variety of scientific backgrounds who have completed their undergraduate training with highest honors and who are interested in tackling cutting-edge research ranging from machine learning to artificial intelligence, with applications to industry, social science, digital transformation or even physics and telecommunications.

Students apply to one of the following flavors:

Data&AI with a computer science flavor

Data&ai with an applied mathematics and statistics flavor, data&ai for social science, data&ai applied to other fields (telecommunication, physics…).

• prepare for a career in artificial intelligence and data science
• dive into an exciting field of research at the confluence of applied mathematics and computer science
• contribute to a domain that will shape tomorrow's societies

The five-year curriculum of the PhD track trains students in cutting-edge research for them to pursue international careers in prestigious universities or leading companies in the inter-disciplinary domain of AI and data science.

The students benefit from a program that is hand-tailored to their needs and interests. Each student is assigned a tutor -- a member of the IP Paris faculty who helps the student establish the courses to follow in each year. Courses can be chosen both in the domain of applied mathematics and in the domain of computer science, as well as beyond these

Relevant courses are offered, e.g., in the Master's programs “ DataAI ” in Computer Science, in “ DataScience “ in Applied Mathematics, or in the neighboring fields of statistics and social sciences.

Students are required to complete one research internship per year for the first two years of the PhD track. This internship can be carried out in the private or public sector, in France or abroad. It represents a maximum of 30 ECTS/year.

Students can complete a semester or a year in a partner university abroad. The modalities and target universities depend on the master's program that the student is assigned to.

• Information Processing and Communications laboratory (LTCI)
• Computer Science Laboratory (LIX)
• Applied Mathematics Center (CMAP)
• Computer Science and System Engineering Laboratory (U2IS)

Admission requirements

Academic prerequisites.

Completion of a Bachelor in computer science or mathematics, at Institut Polytechnique de Paris or equivalent in France or abroad.

Students who have completed the first year of an equivalent program may exceptionally be directly admitted to the second year (4-year PhD program).

Students who already have a master’s degree are invited to apply directly for a PhD in 3 years .

Language prerequisites

A certificate of proficiency in English (level B2) is required (TOEIC, IELTS, TOEFL, Cambridge ESOL), except for native speakers and students who previously studied in English.

How to apply

Applications are exclusively online. You will be required to provide the following documents:

• Transcript 
• Two academic references (added online directly by your referees)  
• CV/resume 
• Statement of purpose

You will receive an answer in your candidate space within 2 months following the closing date of the application session. 

Fees and scholarships

Estimated fees for 2022-2023 are subject to increase

• Regular fees: 243€
• Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
• Special cases: please refer to the "Cost of studies" section of the FAQs

Admission dates

Coordinator.

Louis Jachiet

General enquiry

[email protected]

• People Directory
• Safety at UD

ARTIFICIAL INTELLIGENCE FOR TEACHING AND LEARNING AT UD

Artificial intelligence presents both exciting opportunities and complex challenges for the world, profoundly affecting the ways we live, work, learn and relate to one another., at the university of delaware, our faculty, researchers and students are at the nexus of these rapidly evolving issues every day. this interdisciplinary academic work and cutting-edge research draws on the university’s expertise and resources in data science, computer science, public policy, business, engineering and more. the university also aims to be a leader in the application of ai in the higher education classroom through an innovative tool under development with national partners. the ai for teaching and learning working group provides thoughtful guidance as the university creates and updates its policies and practices related to artificial intelligence., current initiatives, ai for teaching and learning working group.

Created in May 2023, the AI for Teaching and Learning Working Group represents the broad educational landscape of the University. The working group meets monthly to help guide the University of Delaware in the development of innovative practices and sound policies regarding the use of emerging AI tools.

Co-Facilitators

Michael David Evans , Director of Computing Operations, Lerner College of Business and Economics

Meg Grotti , Associate University Librarian for Learning, Engagement, and Curriculum Support

Erin Sicuranza , Director, Academic Technology Services

Joshua Wilson , Associate Professor, College of Education and Human Development

Contact:  [email protected]

Read the UDaily story on the Working Group >

Charge from the provost.

The arrival of open-source artificial intelligence (AI) tools presents both fundamental challenges and exciting new opportunities for institutions dedicated to teaching and learning. Traditional modes of undergraduate instruction and assessment are the most obvious areas for reconsideration, given AI’s powerful generative capacities. Research methods and ethics are also impacted by AI, including the applied research and creative endeavors pursued by our faculty across all disciplines and the graduate students they train. This dynamic and rapidly evolving situation demands careful thought, creativity, and agility as we develop practices that harness the power of AI for teaching and learning while maintaining our commitments to academic integrity in the production and dissemination of knowledge.

The purpose of the AI for Teaching and Learning Working Group is to help guide the University of Delaware over the next two academic years as we develop innovative practices and propose sound policies regarding the use of emerging AI tools. The Working Group will play a key role in fulfilling the University’s partnership with the Ithaka S+R two-year initiative, “ Making AI Generative for Higher Education ,” but its scope is not limited just to that project. In addition, the Working Group is charged with exploring and providing specific guidance on

curriculum development (including general education and First-Year Seminar goals)

the assessment of student learning and program educational goals

academic integrity

research ethics

The Working Group will be formed in Summer 2023 and will operate during the 2023-2024 and 2024-2025 academic years. A key part of its work should be collaborative outreach to faculty, staff, and students beyond the membership of the Working Group, particularly with UD’s AI Center for Excellence, academic senior leadership, the Faculty Senate, the Graduate Council, and the Ithaka S+R “Making AI Generative for Higher Education” initiative. Regular dissemination of the Working Group’s progress should be provided via the monthly Provost’s Digest, a dedicated Web page, and a formal report to the Provost and the Faculty Senate in May 2024 and May 2025.

— May 24, 2023

Working Group Membership

Cory Bart , Associate Professor, Computer and Information Sciences

Stephan Bohacek , Associate Professor, Electrical and Computer Engineering, Computer and Information Sciences

Sunita Chandrasekaran , D&B Mills Career Development Chair, Computer and Information Sciences, and Co-Director, Artificial Intelligence Center of Excellence

Susan Conaty-Buck , Assistant Professor, School of Nursing

Rachel Coppola , Director, Life Design and Career Integration, Career Center

Trevor Dawes , Vice Provost for Libraries and Museums and May Morris University Librarian

Phil Duker , Associate Professor, School of Music

Mike Fernbacher , Assistant Director, Community Standards and Conflict Resolution

Eric Greska , Associate Professor, Kinesiology and Applied Physiology

Kevin Guidry , Associate Director, Office of Educational Assessment, and Assistant Professor, School of Education

Jevonia Harris , Senior Digital Media Programmer, Academic Technology Services

Amy Hicks , Associate Professor, Art and Design

Fred Hofstetter , Professor, School of Education and School of Music

Ethan Kempista , master’s student, College of Engineering

Matt Kinservik , Vice Provost for Faculty Affairs

Julius Korley , Associate Vice President, Office of Economic Innovation and Partnership

Agnes Ly , Associate Professor, Psychological and Brain Sciences

Charissa Powell , Head of Student Success and Curriculum Partnerships Department, Library, Museums and Press

Tom Powers , Associate Professor, Philosophy

Teya Rutherford , Associate Professor, School of Education

Matt Trevett-Smith , Director, Center for Teaching and Assessment of Learning

Dana Veron , Associate Provost, Professor, School of Marine Science and Policy

AI Seminar Series: Navigating the Future of AI and Teaching and Learning in Higher Education

The purpose of this series is to help UD faculty and staff start to grapple with how AI might change the nature of teaching and learning in higher education by understanding foundational concepts related to AI and large language models (LLMs), implications of AI and LLMs within different higher education disciplines, and implications of AI and LLMs on the future of work. 

Multiple in-person and virtual seminars will provide insight into the broad topics of:

AI: Strengths, Limitations, Ethics, and Accessibility

This first part of the series will inform the audience of foundational concepts related to understanding and contextualizing AI in life and society. These concepts relate to understanding what AI and LLMs are and how they work, their strengths and limitations, and ethical considerations related to their construction and utilization.

Implication of AI for Teaching and Learning and the Future of Higher Education

This part of the series will engage the audience in considering how AI and LLMs may change how we teach and, considering the topics in Part 2, what the future of higher education looks like. 

Preparing Students for the Future of Work

These sessions will engage the audience in considering how AI and LLMs are changing the nature of work in major industries, and the implications of those changes on how we prepare students to enter those fields. Sessions in this part of this will illustrate current initiatives taken in certain disciplines to provide AI-related education and training to UD students and/or sharing debates and concerns within a field/program/department. 

Most seminars are hybrid events, and registration is required. When registering, please denote whether you plan to attend virtually or in person.

Upcoming seminars:

Virtual ai information and demo session for faculty.

Are you curious about generative artificial intelligence, how it can be leveraged in your classroom, and what pitfalls and promises it might hold for student learning and your teaching? Join the UD AI Working Group to explore the opportunities and challenges of integrating generative AI into your teaching practice. This virtual event will highlight insights gathered throughout an AI seminar series held this past year and feature lightning talks by UD faculty who have firsthand experience implementing these tools into their teaching. Following the presentations, you will have the opportunity to discuss the pros and cons of adopting generative AI tools with a community of your peers. The session will provide you with a grounding in the functions of at least on generative AI teaching tool and how those functions can contribute to an improved teaching, learning or assessment experience for your students. This session will be offered on three dates:

• April 24 from 12-1:30 p.m.  Register here .
• April 29, 10:30 a.m.-12 p.m . Register here .
• May 3, 1-2:30 p.m.  Register here .

Implications of AI on Training the Next Generation of Healthcare Workers and Business Leaders

May 2, 1-2 p.m., hybrid

Generative AI is profoundly impacting Human Resource (HR) development. Using a framework drawn from the presenter's co-edited book project on this topic, this seminar delves into the evolving role of AI in HR, highlighting both the opportunities and challenges it presents. It will cover key topics like personalized learning, adaptive training for HR professionals, and ethical considerations of AI in HR. The discussion extends to future trends and the integration of AI into HR training. Concluding, the seminar stresses the need for curriculum adaptation and continuous learning in response to an AI-enhanced future. This exploration of generative AI's impacts on HR offers insights into AI's broader implications for workforce preparation, emphasizing the need for universities to adapt their educational approaches accordingly. This is the final seminar for the 2023-24 academic year.

Register here .

Previous seminars:

Large language models — challenges and opportunities.

Sept. 26, 2023 —  Large language models (LLM) such as ChatGPT are changing many aspects of our lives, such as how we access online information and how we learn. This talk explored the mystery of LLMs and discussed the challenges and opportunities in this new era of AI.  

Watch the recording of the seminar here

Ethical Considerations in AI

Oct. 19, 2023 —  This seminar presented two engaging talks. One delved into ethical and conceptual issues surrounding Generative AI, particularly Large Language Models (LLMs), in the context of research misconduct. The second explored the duty of teachers to support students in learning to use AI and to resist the temptation to limit teaching to the tools that happened to be available in the past.

Watch a recording of the seminar here .

Pitfalls and Promise: Generative AI, Disability, and Access

Nov. 6, 2023 —  This talk considered the duality of two realities disabled people face in the context of their everyday lives, and specifically, generative AI. The first, considerations of people with disabilities and our experiences are often left out of decision-making processes which produce policy, like those being made within the university and individual classrooms. The second, we are often the "test market" and expert users of many tools used by the masses to gain more physical and electronic access to the world around us, like closed captioning, descriptive audio, voice to text, and other predictive and generative AI tools. 

Navigating the AI Landscape: A Framework for Evaluating Assessment Tools in Higher Education

Nov. 29, 2023 —  Advanced automated tools, including generative AI open a world of new possibilities for assessing student learning. They can provide immediate, personalized feedback. But determining if a particular tool should be used is complex with many potential questions and decisions. The presenters shared a draft framework that will help faculty make well-informed decisions about the use of AI tools to assess student learning.

Watch a recording of this seminar here . Download the slides  here .

AI and the Future of Higher Education

Dec. 7, 2023 — The  integration of AI into academia presents unparalleled opportunities for enhancing core university missions of teaching and research, but it also poses significant challenges, from misinformation threats to concerns about equity, intellectual property, and undermining critical thinking. The presenters surveyed the national landscape to see how public research universities are responding to the moment and consider how UD can promote the AI literacy of our students and harness AI's transformative power responsibly.

Watch a recording of this seminar here .

AI and AI – The Intersection of Artificial Intelligence and Academic Integrity

Jan. 17, 2024 —  This session addressed how students can use AI while still upholding standards of academic integridy and how faculty can define and encourage appropriate use of AI. This session also covered how faculty may identify inappropriate use of AI as well as the range of response options when AI-assisted academic dishonesty occurs.

Ethical Use of AI Tools for Research and Publishing 

Feb. 15, 2024 —  New tools for quickly and easily finding research publications are exploding in popularity and are being used extensively on campus right now. AI can be controversial: Is it ethical? Does it replicate bias? Can using it infringe copyright? Sometimes the answers to these questions are complicated. This seminar covered effective ways for thinking through these questions when you are exploring particular tools. 

A video recording of this seminar will be posted here when it becomes available.

AI-Powered Career Advancement: Navigating Career Preparation, Recruitment, and Lifelong Learning

Feb. 28, 2024 —  Lifelong learning and career preparedness are crucial in our fast-paced, technology-driven economy. Predictions about job security have rapidly shifted over the past few years, highlighting the importance of adaptability, skill development, critical thinking, and problem-solving for both traditional and emerging roles. This session explored the best practices for coaching students in their use of Generative AI in career exploration and job/internship searching as well as how employers utilize Generative AI for recruitment. The session covered how we can support students to be enterprising in their learning for career exploration and reskilling throughout their lives.

Watch a recording of this session here .

AI Literacy Tutorial

The AI Literacy Tutorial was developed by the UD Library, Museums & Press as a resource for students and faculty to start a conversation about appropriate use of artificial intelligence.

The tutorial provides an overview of how large language models (LLM) like ChatGPT work, the limitations of their use, and ethical considerations for the appropriate use of LLMs. Specifically, it describes hallucinations (i.e., when the LLM generates false information), asks you to consider bias embedded within AI, and the appropriate uses for AI tools.

The tutorial takes about 30-40 minutes to complete.

How can faculty use the tutorial?

To spur a discussion between you and your students acknowledging AI tools and education

To help you and your students collaboratively craft a policy about appropriate and inappropriate uses of AI tools within your specific course

To introduce content about bias

To introduce content about ethics

How can students use the tutorial?

To learn the basic concepts of AI

To increase awareness of the appropriate and inappropriate uses of AI

To consider bias, ethics and other considerations related to AI

AI Literacy & Tools: Managing AI Use in Your Classroom

Nov. 16, 2023 — This workshop featured  common AI terminology, development of assignments that promote the ethical use of AI, syllabus template language for the purposes of using or not using chatbots for learning, and faculty- and student-centered resources for understanding and teaching with chatbots.

Complete the AI Literacy Tutorial >

Making ai generative for higher education.

The University of Delaware's AI for Teaching and Learning Working Group is part of a two-year partnership with 19 universities to assess AI’s impact on higher education and evaluate institutions’ readiness to implement the technology. 

"Making AI Generative for Higher Education Project" Announcement >

Read the preliminary summary and recommendations from this project >, guiding considerations and framework, this resource provides educators with guidance regarding ways to utilize ai within teaching and learning in ways that are effective, ethical, and equitable and that will empower educators and students alike.  , these are considerations, not requirements. educators do not have to teach with or about ai, but there are good reasons to do so, and should someone wish to, here are some things to consider., guiding considerations for ai in teaching and learning >, in the news.

Exploring AI Innovation

Through collaboration with Amazon Web Services, UD will offer students unique digital learning tools and services.

Artificial Intelligence in Teaching and Learning

UD leads the conversation about AI in the context of teaching and learning with an interdisciplinary UD working group and other initiatives.

Artificial Intelligence

In a study and survey, UD researchers found that respondents had a variety of opinions — and levels of awareness — regarding artificial intelligence.

Exploring New Tools for the Health Care Industry

University of Delaware alum Sarah Levine studied ways that artificial intelligence could help to improve health care.

Harnessing Artificial Intelligence for the Masses

Distinguished Professor Rudolph Eigenmann is part of a $20 million National Science Foundation-funded project to expand access to artificial intelligence.

Additional Links

Artificial intelligence center of excellence, center for teaching and assessment of learning, ud study aide, connect with the working group, email the ai for teaching and learning working group >, upcoming events.

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MSc Artificial Intelligence

By enrolling in this MSc Artificial Intelligence undergraduate programme, you'll become a master of AI, gaining the skills and knowledge Read more...

• 12 months Online degree: £9,750 per year (UK)
• 18 months Online degree: £6,500 per year (UK)
• 24 months Online degree: £4,875 per year (UK)

Artificial Intelligence online MSc

University of bath online.

This online MSc in Artificial Intelligence is ideal if you are motivated by the challenges and infinite possibilities of advancing AI. It Read more...

• 27 months Online degree

University of Liverpool Online Programmes

University of liverpool.

Programme Description Pursue a career in this high-demand sector while gaining a deep and systematic understanding into the domains of Read more...

• 30 months Online degree: £16,065 per year (UK)

University of Essex Online

Break down the walls of traditional technology with this innovative conversion degree. Our MSc Artificial Intelligence is designed for Read more...

• 2 years Online degree: £6,084 per year (UK)

Online MSc Computer Science with Artificial Intelligence

University of wolverhampton.

An entirely online master’s for forward-thinking individuals who may not have a background in computer science. The 100% online MSc Read more...

• 14 months Online degree: £5,657 per year (UK)

Artificial Intelligence MSc (online)

University of bath.

• 27 months Online degree: £5,776 per year (UK)

Computer Science with Artificial Intelligence MSc

Northumbria university, newcastle.

Course Drive digital change with our distance learning Computer Science with Artificial Intelligence MSc. With the growth in the Read more...

• 2 years Distance without attendance degree: £4,980 per year (UK)

Data Science and Artificial Intelligence (online) MSc

Msc computer science with artificial intelligence, university of hertfordshire.

Elevate your prowess with UH Online's MSc Computer Science with Artificial Intelligence. Unleash the power of AI programming and machine Read more...

• 24 months Online degree: £4,500 per year (UK)

Artificial Intelligence MSc (Distance Learning)

University of huddersfield.

Jumpstart a rewarding career in Artificial Intelligence (AI) with our AI MSc Distance Learning course at The University of Huddersfield. Read more...

• 2 years Distance without attendance degree: £7,920 per year (UK)

Artificial Intelligence MSc

University of leeds.

Designed and delivered by computer scientists, this innovative online Masters in Artificial Intelligence has been developed specifically Read more...

• 24 months Online degree: £7,500 per year (UK)

MSc Computer Science with Artificial Intelligence Online

University of york.

Artificial Intelligence and machine learning are becoming ubiquitous. It is everywhere, from recognising you in a photo, giving you advice Read more...

• 2 years Online degree: £9,000 per year (UK)

MSc Machine Learning and Data Science (Online)

Imperial college london.

Accelerate your career in engineering or data science on this online and part-time Master's course. Via hands-on projects, you'll build Read more...

• 2 years Online degree: £17,175 per year (UK)

MSc Human-Computer Interaction (Online)

University of bristol.

Designing interactive products and services to support our increasingly digital lives requires a deep understanding of human capabilities, Read more...

• 2 years Online degree: £8,200 per year (UK)

Keele University

The online MSc Computer Science with Artificial Intelligence from Keele University has been carefully designed to develop the knowledge and Read more...

• 2 years Online degree: £3,720 per year (UK)

Abertay University

Specialise in the fast-growing field of artificial intelligence The use of artificial intelligence (AI) within the modern business Read more...

• 1 year Online degree: £6,600 per year (UK)

MSc Cybercrime

University of portsmouth online.

Cybercrime is the term applied to criminal and harmful behaviours that are facilitated using digital technologies, or that only occur due Read more...

• 2 years Online degree: £4,700 per year (UK)

Data Science and Artificial Intelligence MSc, PGDip, PGCert - Online Course

University of liverpool online.

Explore the domains of artificial intelligence and data science through this conversion degree. The MSc programme is accredited by the Read more...

• 30 months Online degree: £6,120 per year (UK)

In the age of intelligence, your understanding bridges the gap to tomorrow. This programme is accredited by The British Computing Society, Read more...

• 24 months Online degree: £4,950 per year (UK)

Artificial Intelligence MSc, PGDip, PGCert - Online Course

Expand the horizons of intelligent systems and develop your skills in this rapidly growing sector with our specialist online master’s Read more...

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PhD in Artificial Intelligence at Alma Mater

Explore the frontiers of AI at Alma Mater Europaea with our cutting-edge PhD program in Applied Artificial Intelligence

Alma Mater Europaea is proud to offer an immersive and research-focused PhD program in Applied Artificial Intelligence (AI). Our fully accredited program aims to shape the next generation of innovators, researchers, and thought leaders who will drive the future of AI technology.

The Alma Mater's PhD in Applied AI is deeply research-oriented. Doctoral candidates will get an opportunity to work on a variety of projects under the mentorship of our distinguished faculty. Doctoral supervisors include members of the European Academy of Sciences and Arts, which unites two thousand leading scholars, 34 of whom are Nobel Prize winners. 

In this 3-year doctoral program, which will start in October 2023, students intensely work on a research project resulting in an AI application and prepare doctoral dissertations. The program is suitable for both full-time students and those who work. You can study from any time zone or study while working in one of our partner corporations and research labs throughout the European Union. 

Studying is blended with weekly online sessions and intense week-long in-person meetings in Europe and US once per semester. The study language is English.

Our curriculum is designed to provide students with a comprehensive understanding of AI, including machine learning, deep learning, natural language processing, computer vision, robotics, and AI ethics. It is curated by a globally renowned faculty, pioneering research in AI, and is regularly updated to reflect the rapid advancements in the field.

The Applied AI PhD study is open to citizens or residents of any country who have previously completed bachelor's or master's degree studies or equivalent in any discipline, which is typically received after four or five years of higher education. The candidates who do not have a background in Computer Sciences, AI or related fields take introductory training in computer science and applications of AI in areas such as management, economics, public policy, medicine, or others, in the first few months of their studies. 

Unleash Your Potential - Combine the Development of Your AI Solution with a PhD 

We believe in merging academic rigor with practical innovation. We are excited to invite experts with revolutionary ideas to integrate the development of their AI solutions into our prestigious PhD program in Applied Artificial Intelligence. Selected candidates with promising ideas will receive full scholarships and offer to develop their proposals in top-of-the-line corporations and research labs. 

This unique opportunity provides a platform to amplify your ideas' impact and gain recognition as an influential thought leader in the AI landscape.

Pursuing a PhD while developing your AI solution offers you the best of both worlds:

In-depth Theoretical Knowledge : Our PhD program offers comprehensive training in cutting-edge AI theories and technologies. You'll gain a solid understanding of machine learning, deep learning, natural language processing, computer vision, robotics, and AI ethics, equipping you with the foundation to optimize and enhance your solution.

Expert Guidance : You will have access to world-renowned faculty who can provide invaluable guidance and insight as you refine your AI solution. They'll help you navigate challenges and uncover opportunities for innovation that you may not have considered.

Access to Resources : As a PhD student, you'll have access to state-of-the-art resources, including supercomputing facilities, AI tools, and vast research databases, to support your solution development.

Collaborative Environment : Join a vibrant community of other AI experts and PhD students, providing a rich network for collaboration, brainstorming, and problem-solving.

Academic Recognition : As you work on your AI solution, you will contribute to scholarly knowledge in AI. Your efforts could lead to groundbreaking research papers and recognition within academic and industry circles.

Career Advancement : Combining a PhD with developing an AI solution can enhance your career trajectory. It can open up opportunities for tenure-track positions, leadership roles in tech companies, or even starting your own AI venture.

We encourage you to bring your ideas to life and create AI solutions that can change the world. Let's redefine the future of AI together at Alma Mater Europaea.

Grants, scholarships and partnership opportunities

Alma Mater Europaea is pleased to announce full-tuition research grants, partnerships and teaching assistantships for Applied AI projects. Global corporations, industry leaders and influential research groups worldwide will be involved in financing doctoral projects.

If you have a specific Artificial Intelligence project or research topic that you care deeply about and are interested in studying it from a theoretical angle, we invite you to combine your work with a doctorate.

Grants are available in 120,000 Euros for three years (40,000 Euros per year) and more. Selected proposals will have the opportunity to partner with global AI industry leaders and may develop million-dollar world-changing solutions. Read our Grants page to know more about these opportunities.

"The future of humanity is closely connected with artificial intelligence research," stressed Klaus Mainzer, the European Academy of Sciences and Arts president and author of the Artificial Intelligence: When do Machines Take Over? (Springer 2020), in March 2023 when Alma Mater announced the Applied AI PhD program.

"Our doctoral researchers will be in the center of the innovation and discovery. Our university will support you in presenting your research at top-tier conferences, publishing in high-impact journals, and creating a strong professional network within the global AI research community. We believe in pushing the boundaries of human knowledge and making impactful contributions to society," said Ludvik Toplak, the Alma Mater Europaea ECM president.

Join us in this exciting journey to redefine the world's future. Read more about the doctoral program, grant & partnership opportunities, and register for our live online presentation. Stay updated by following us on LinkedIn  and Twitter .

Undergraduate/Bachelor

Graduate master's, application & admission.

PhD in Artificial Intelligence

Presentation.

The main source of information about all PhDs at UPC  can be found at the Doctoral School

This page contains some complementary and specific information.

Acceptance Profile:

While the legal requirements to access any PhD can be found in the webpage of the Doctoral School, the ideal background of a candidate for our program is a degree in Computer Science  and a Master in Artificial Intelligence. However, students with degrees and masters in related areas such as Engineering, Mathematics, Statistics, etc may be accepted.

All candidates must have before being accepted a letter from a teacher in the program agreeing in supervising the PhD. The program coordinator can help students in finding a potential advisor that best fits their interest.

Accepted students must be able to read technical documents and attend seminars and conferences in English. Candidates must proof having level B1 of English.

Organization

Program structure and activities

Annual Evaluation

Evaluation of supervised research periods

Public Defense of Research Plan

Research Plan Public Defense

Thesis document and defence

Thesis document: review, deposit, and defense

Budget for the examination panel

PhD degree request

PhD theses list

PhD Theses list

Research groups

Grants and scholarships

Academic calendar

Regulations

Document for the annual assessment of student progress during the academic course [pdf-form]

Annual report of student activities along the academic course

Annual report of student progress during the academic course (advisor/tutor) [pdf-form]

Statement of commitment between doctoral candidate and thesis supervisor

Related publications form for the thesis deposit

Proposal of the thesis examination panel (in Catalan) [Model T - rtf]

Quality report and authorization of the thesis (in Catalan) [Model ADU - doc]

Registration form for doctorate studies

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Artificial Intelligence (AI), Machine Learning and Data Science

On this page, artificial intelligence (ai), machine learning and data science news feed, zhao receives the graduate school dean’s distinguished dissertation award.

April 12, 2022 Dr. Bingxin Zhao, a doctoral graduate from the Gillings School's Department of Biostatistics, is one of four recipients of The Graduate School's 2022 Dean’s Distinguished Dissertation Award.

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Looking to break into A.I.? These 5 schools offer master’s in artificial intelligence programs

While buzz about artificial intelligence (AI) has largely focused on the growing popularity of generative AI tools such as ChatGPT, the demand for jobs and growth in the sector is booming. In fact, AI and machine learning specialist roles are growing faster than any other occupation in the world, according to the World Economic Report’s Future of Jobs Report .

Ryan Aytay, CEO of Tableau , says AI and big data’s rapid growth in popularity and growth has created a need for everyone to learn the appropriate skills as well as to more broadly adopt a philosophy of lifelong learning.

Embark on an AI revolution with UT Austin. Two course start dates per year. Accessible $10,000 tuition.

 “[AI] only seems to have accelerated this need for everyone, not just business users, not just analysts, really everyone to have the ability to not only see and understand but also use that data to make decisions with regardless of what they need to be focused on,” Aytay says.

Over the past few months, more universities have sought to meet the AI demand head on by creating degree programs specifically focused on the subject. For example, just in March 2024, Purdue University—a school known for its strong engineering arm— announced a brand new online master’s in AI . 

If AI from a business perspective interests you, you’re in luck, too. Many business schools now offer MBA specializations in AI as well as certifications focused on the subject.

And while there are also options to take free online courses in artificial intelligence , many schools now offer full-fledged degree tracks. Fortune compiled a list of five master’s in AI programs to check out if you’re looking to make a career switch. 

Duke University

At Duke University , students in the artificial intelligence for product innovation master of engineering program can complete courses in -person in 12 to 16 months or online within 24 months. Students can also choose from a variety of learning tracks—or a focus—including data science and machine learning. 

The program also includes a capstone project and summer internship. Graduates often move intotake jobs as machine learning engineers, AI engineers, data scientists, and data engineers for companies including OpenAI, Doordash, and Target’s AI Lab within six months of graduation. All students must complete an online data science and Python bootcamp the summer before the start of their program.

Students complete 10 courses during the program, covering topics including AI, machine learning, operations, and management. The management courses are offered through Duke’s Law School and Fuqua School of Business, which Fortune ranks as having one of the top full-time MBA programs in the U.S.  

Applicants are expected to have an undergraduate degree in science or engineering (or equivalent technical work experience), minimum one year of programming experience, two semesters completed of calculus, and meet English proficiency admission requirements (for international students). 

The cost of Duke’s program depends on the modality (online or in-person) and the amount of time taken to complete the degree. Applications require transcripts, short-answer essay responses, a resume, three letters of recommendation, and an introductory video. Prospective students have the option to submit GRE scores.

Format : Online or in-person

Cost: $99,734 (online); $113,892 (in-person)

Deadlines: Round 1: January 15 (online and in-person); Round 2: March 15 (in-person), April 15 (online) 

Johns Hopkins University

Johns Hopkins University offers both a master’s degree and a graduate certificate in artificial intelligence through its Whiting School of Engineering . The online master’s in AI includes 10 courses—four core courses and six electives—and students can take up to five years to complete them. 

Curriculum includes algorithms, applied machine learning, and creating AI-enabled systems. Johns Hopkins does require several prerequisite courses including calculus, programming, and linear algebra, but will offer provisional admission for students to complete the required courses prior to enrollment. 

GRE scores aren’t required to apply, but most admitted students have at least a 3.0 undergraduate GPA. 

Format : Online

Cost: $52,700 (estimated total program price)

Deadlines: Open year-round (terms begin in spring, summer, and fall)

Northwestern University

Northwestern University’s master’s in artificial intelligence seeks to train those with a desire to “become architects of intelligent systems.” Through the program, students learn the psychological and design implications of AI and how business needs may be satisfied.

Students can take a traditional track or choose the MSAI+X program and combine AI with their original field of study. The program is limited to approximately 40 students per year and lasts for 15 months. 

Applicants should have a bachelor’s in computer science or related field, and preference will be given to those with at least two years of relevant work experience.

Format : In-person

Cost: ~ $110,000

Deadlines: December 15 (priority); March 15 (final)  

Purdue University

Purdue’s new master’s in artificial intelligence seeks to prepare students to succeed in today’s increasingly tech-reliant world. Students will learn practical skills in AI and computing as well as professional skills like leadership and project management and technical skills like programming and machine learning. 

Participants can choose two major tracks: AI and machine learning or AI management and policy. Admissions requirements differ depending on which major is chosen. There is no application fee to apply. While English proficiency testing is required for international students, GRE and GMAT scores are not needed.

Cost: ~ $28,000

Deadlines: August 1 (fall); December 1 (spring); April 1 (summer)

University of Texas—Austin

UT—Austin offers its online master’s program in AI through its department of computer science and machine learning laboratory , and the degree can be completed at your own pace. The degree covers about two years worth of content. The program is offered on the online education platform, edX , an online education platform, and costs $10,000 to complete, making it one of the more affordable options.

The degree covers AI-related topics, including natural language processing, reinforcement learning, computer vision, and deep learning, which prepares graduates for A.I. jobs in engineering, research and development, product management, and consulting.

The program quickly skyrocketed in popularity , with more than 4,000 prospective students requesting more information from the university within 24 hours of its launch announcement. 

Prospective students must submit an application to the Graduate School at The University of Texas at Austin as well as a statement of purpose, resume, and transcripts. Letters of recommendation and GRE scores are optional to submit. 

Cost: $10,000 (2023–-24 academic year)

Deadlines: Fall: April 1 (priority), May 1 (final); Spring: August 15 (priority), September 15 (final)

Is a masters in AI worth it?

Yes, having a master’s in AI can be very beneficial for those wanting to become AI experts. However, it is also important to keep in mind that AI is always evolving. By the time your program completes, some of the skills and best practices you initially learned could be out of date. 

Do master’s in AI require coding?

Yes, you will need to learn how to code if you plan to study AI in an advanced degree program. Python is generally considered to be the most relevant programming language to AI . Having skills in Java, SQL, C++, and R also couldn’t hurt. Some master’s in AI programs, like Duke, require students to have some programming experience as well as to enroll in a Python bootcamp.

Which master’s degree is best for AI?

The best degree pathway for those interested in AI truly depends on your interests. 

A master’s in AI will likely give you a perfect entry into careers in AI, data science, machine learning, and beyond. If you know a particular specialization in the tech space interests you more than another, that is a great place to start. Above all, keep in mind that because AI master’s are new, there is no perfect path; it’s up to you to define it.

Check out all of Fortune’ s rankings of degree programs , and learn more about specific career paths .

Sydney Lake contributed to this piece.

Rethink AI and its potential to innovate in this 6-week online course from MIT Sloan.

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Examine the technology behind AI over 6 weeks on this Oxford online programme.

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• UTCS Direct

New Virtual Master’s Program in AI Breaks Traditional Learning Methods

Submitted by Anonymous on Tue, 02/20/2024 - 10:00am

by Cassandra Ozuna

The newly introduced Online Master's in Artificial Intelligence (MSAI) program at the University of Texas at Austin is strategically designed to meet the dynamic needs of the AI sector while placing a strong emphasis on ethical considerations. Throughout the program, students are immersed in challenging coursework, including a compulsory "Ethics in AI" course that underscores the importance of responsible AI utilization, incorporating assignments featuring AI tools such as ChatGPT. Boasting a flexible schedule and top-notch instructional videos, the program caters to a diverse student body worldwide, nurturing an active community via platforms like Slack and Discord. As UT's fastest-growing online master's offering, the MSAI not only imparts essential AI skills but also primes students for the swiftly expanding job market. This is particularly significant given projections indicating a staggering 97 million new AI-related positions within the next two years, underscoring the program's dedication to global knowledge dissemination.

Read the full article authored by Melanie Faz at The Daily Texan

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PhD Proposal: Multi-Agent Autonomous Decision Making in Artificial Intelligence

Multi-Agent Autonomous Decision Making, especially Multi-Agent Reinforcement Learning (MARL), is an emerging area of Artificial Intelligence (AI) where autonomous agents interact with each other, fostering competition and/or cooperation in real world problems like climate change, supply chains, self-driving cars, sports, interdiction games, war games among other uses.The PhD proposal has been arranged considering the research works already carried out and published and those are continuing in the areas of AI Alignment and Social Cooperation of Autonomous AI Agents, Speedup and Scalability for Efficient Multi-Agent AI, Multi-Agent Explainable AI and Unlearning to Build Trustworthy AI and Real World Applications of Multi-Agent AI for Climate Conservation, Global Supply Chain orchestration etc.

Examining Committee

Dr. John Dickerson

Department Representative:

Dr. Tom Goldstein

Dr. Hal Daumé

Data Science & AI

Syllabi for MS/PhD Interview & Entrance Test

The written test will have two parts:

• Theory – These will be objective questions (MCQ, Fill in the blanks, True/False etc)
• Python Coding – 2 problems that you will be required to write a code for in Basic Python

Theory Syllabus

Probability and statistics.

– Counting (permutation and combinations) – independent events, mutually exclusive events – marginal, conditional and joint probability – Bayes Theorem – conditional expectation and variance – mean, median, mode and standard deviation – correlation, and covariance – random variables, discrete random variables and probability mass functions – uniform, Bernoulli, binomial distribution – Continuous random variables and probability – distribution function, cumulative distribution function, Conditional PDF – uniform, exponential, Poisson, normal, standard normal, t-distribution – chi-squared distributions – Central limit theorem – confidence interval – z-test, t-test,chi-squared test.

Linear Algebra

– Vector space, subspaces – linear dependence and independence of vectors – matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix – quadratic forms – systems of linear equations and solutions – Gaussian elimination – eigenvalues and eigenvectors – determinant, rank, nullity – projections – LU decomposition, singular value decomposition.

Calculus and Optimization

– Functions of a single variable – limit, continuity and differentiability – Taylor series – maxima and minima – optimization involving a single variable.

Programming, Data Structures and Algorithms

– Programming in Python – Basic data structures: stacks, queues, linked lists, trees, hash tables – Search algorithms: linear search and binary search – Basic sorting algorithms: selection sort, bubble sort and insertion sort – Divide and conquer: mergesort, quicksort – Introduction to graph theory – Basic graph algorithms: traversals and shortest path

Coding Syllabus

You will be given some coding tasks that you need to complete and execute by writing Python scripts. To be able to do this you will need to know the following:

– Basic Python syntax – comments, variables, basic data types – Operators and Control Flow – If/else, for, while, range, break, continue, pass = Functions – How to define and use them – Lists/Arrays, Tuples, and associated methods

================================================================

Interview Topics

For those who qualify after the written test for the online interview, questions from the following additional topics may be asked during the interview

For MS/PhD Interviews

Machine learning.

– Supervised Learning regression and classification problems – Simple linear regression – Multiple linear regression – Ridge regression – Logistic regression – k-nearest neighbour – Naive Bayes classifier – Linear discriminant analysis – Support vector machine – Decision trees – Bias-variance trade-off – Cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network – Unsupervised Learning: clustering algorithms

Artificial Intelligence (AI)

– Search: informed, uninformed, adversarial – Logic: Propositional Logic, Predicate Logic – Reasoning under Uncertainty Topics – Conditional Independence Representation – Exact Inference through Variable Elimination – Approximate Inference through Sampling

PhD applicants may also be asked questions from specialized topics for the interview – They can select a topic from Deep Learning, NLP, Vision, RL, Time-Series modeling depending on their interest and background.

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