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Master’s in Artificial Intelligence & Machine Learning Online
Master of Science in Artificial Intelligence & Machine Learning
Drexel University’s online MS in Artificial Intelligence & Machine Learning is an interdisciplinary program structured around three focus areas: data science and analytics, theory of computation and algorithms, and applications of artificial intelligence and machine learning. Designed for current practitioners, you’ll work with real datasets and state-of-the-art tools and systems to build knowledge and experience that can be used immediately in the workplace.
A strong background in computer science is required for this program. For those who do not have a bachelor’s or master’s degree in computer science, Drexel’s Graduate Certificate in Computer Science can serve as the entry point into the program.
The 45-quarter credit MS in Artificial Intelligence & Machine Learning is housed in Drexel’s College of Computing and Informatics. Faculty have active research experience in machine learning, computer vision, game AI, data science, cognitive science, high performance computing, software engineering, applied machine learning in gaming, and applied machine learning in security.
The MS in Artificial Intelligence & Machine Learning will prepare you to:
- Analyze a problem and identify and define the use of artificial intelligence and/or machine learning as appropriate to its solution
- Understand the implementation and use of existing artificial intelligence and/or machine learning tools and systems
- Apply mathematical foundations, algorithmic principles, and computational knowledge in the modeling and design of artificial intelligence and machine learning systems
- Design, implement, and evaluate a computer-based artificial intelligence and machine learning system, process, component, or program to meet a specific need
- Apply sound software engineering principles in the construction of computer-based artificial intelligence and machine learning systems
- Understand the ethical aspects of artificial intelligence and machine learning, and communicate these aspects as part of result interpretation
- Understand and communicate the legal and ethical aspects of using artificial intelligence and machine learning in societal contexts
What is an MS in Artificial Intelligence?
The MS in Artificial Intelligence covers theories and principles in artificial intelligence – a branch of computer science that focuses on creating machines that can perform tasks with human-like intelligence.
Is a Master’s Degree Required For Machine Learning?
While a master’s degree is not required for machine learning, it can help you stand out in the field, especially if you don’t have previous work experience in machine learning or AI. Drexel’s program focuses specifically on artificial intelligence and machine learning foundations, algorithms, and systems.
What Can You Do with a Master’s in Artificial Intelligence?
According to a 2018 LinkedIn report, AI skills were among the fastest-growing skills on the platform, and there was a 190% global increase from 2015-2017.
Common jobs within the industry include:
- Data Scientist
- Software Engineer
- Deep Learning Engineer
- Algorithm Developer
- Computer Vision Engineer
How Much Can You Earn with an MS in Artificial Intelligence & Machine Learning?
Artificial Intelligence & Machine Learning Career Opportunities
Job Title
Salary*
Data Scientist
$129,000
Computer and Information Research Scientist
$133,750
Computer Systems Analyst
$111,350
Market Research Analyst
$87,550
Operations Research Analyst
$89,300
*Data from Wanted Analytics
How Long Does It Take to Complete an MS in Artificial Intelligence?
The MS in Artificial Intelligence & Machine Learning can be completed on either a full- or part-time basis. You can complete the degree in as little as two years.
Unlike many universities, most of Drexel’s programs operate on a quarter system. Each quarter term is 10 weeks long, and there are four quarters in Drexel’s academic year. To learn more about Drexel’s quarter system, check out our online guide.
Learn From the Best – Can Artificial Intelligence Have a Bias?
Edward Kim, associate professor of computer science, explains how AI can be biased and what we can do to fix it.
Drexel University offers a variety of Graduate Minors that can be added to any master's degree program.
State restrictions may apply to some programs.
Curriculum
This program is organized into four 10-week quarters per year (as opposed to the traditional two semester system) which means you can take more courses in a shorter time period. One semester credit is equivalent to 1.5 quarter credits.
| Core Courses | ||
| CS 591 | Artificial Intelligence and Machine Learning Capstone I | 3.0 |
| CS 592 | Artificial Intelligence and Machine Learning Capstone II | 3.0 |
| Choose appropriate core courses for concentration: | 9.0 | |
| Applied |
||
|
CS 501
|
Introduction to Programming | |
|
or CS 570
|
Programming Foundations | |
|
CS 614
|
Applications of Machine Learning | |
|
INFO 629
|
Applied Artificial Intelligence | |
| Computational |
||
|
CS 510
|
Introduction to Artificial Intelligence | |
|
CS 613
|
Machine Learning | |
|
CS 615
|
Deep Learning | |
| Breadth Requirements | 9.0 | |
| One course must be selected from each group for the appropriate concentration | ||
|
Applied
|
||
|
Data Science Foundations
|
||
|
DSCI 501
|
Quantitative Foundations of Data Science | |
|
DSCI 511
|
Data Acquisition and Pre-Processing | |
|
DSCI 521
|
Data Analysis and Interpretation | |
|
DSCI 631
|
Applied Machine Learning for Data Science | |
|
DSCI 632
|
Applied Cloud Computing | |
|
INFO 546
|
Data Analytics for Community-Based Data and Service | |
|
INFO 623
|
Social Network Analytics | |
|
INFO 634
|
Data Mining | |
|
INFO 659
|
Introduction to Data Analytics | |
|
AI Foundations
|
||
|
CS 502
|
Data Structures and Algorithms | |
|
CS 503
|
Systems Basics | |
|
CS 510
|
Introduction to Artificial Intelligence | |
|
CS 613
|
Machine Learning | |
|
INFO 612
|
Knowledge-based Systems | |
|
INFO 692
|
Explainable Artificial Intelligence | |
|
Human-Centered Computing
|
||
|
CS 661
|
Responsible Data Analysis | |
|
CT 620
|
Security, Policy and Governance | |
|
INFO 508
|
Information Innovation through Design Thinking | |
|
INFO 590
|
Foundations of Data and Information | |
|
INFO 608
|
Human-Computer Interaction | |
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INFO 615
|
Designing with Data | |
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INFO 616
|
Social and Collaborative Computing | |
|
INFO 690
|
Understanding Users: User Experience Research Methods | |
|
INFO 691
|
Prototyping the User Experience | |
|
INFO 693
|
Human–Artificial Intelligence Interaction | |
|
INFO 725
|
Information Policy and Ethics | |
|
Computational
|
||
|
Data Science and Analytics
|
||
|
CS 660
|
Data Analysis at Scale | |
|
CS 661
|
Responsible Data Analysis | |
|
DSCI 511
|
Data Acquisition and Pre-Processing | |
|
DSCI 521
|
Data Analysis and Interpretation | |
|
DSCI 631
|
Applied Machine Learning for Data Science | |
|
DSCI 632
|
Applied Cloud Computing | |
|
INFO 546
|
Data Analytics for Community-Based Data and Service | |
|
INFO 623
|
Social Network Analytics | |
|
INFO 634
|
Data Mining | |
|
INFO 659
|
Introduction to Data Analytics | |
|
Algorithmic Foundations
|
||
|
CS 521
|
Data Structures and Algorithms I | |
|
CS 522
|
Data Structures and Algorithms II | |
|
CS 525
|
Theory of Computation | |
|
CS 567
|
Applied Symbolic Computation | |
|
CS 618
|
Algorithmic Game Theory | |
|
CS 620
|
Advanced Data Structure and Algorithms | |
|
CS 621
|
Approximation Algorithms | |
|
CS 650
|
Program Generation and Optimization | |
|
DSCI 501
|
Quantitative Foundations of Data Science | |
|
ECES 521
|
Probability & Random Variables | |
|
ECES 523
|
Detection & Estimation Theory | |
|
MATH 504
|
Linear Algebra & Matrix Analysis | |
|
MATH 510
|
Applied Probability and Statistics I | |
|
Applications of AI/ML
|
||
|
CS 614
|
Applications of Machine Learning | |
|
CS 583
|
Introduction to Computer Vision | |
|
CS 610
|
Advanced Artificial Intelligence | |
|
CS 611
|
Game Artificial Intelligence | |
|
CS 612
|
Knowledge-based Agents | |
|
CS 618
|
Algorithmic Game Theory | |
|
CS 630
|
Cognitive Systems | |
|
CS 634
|
Advanced Computer Vision | |
|
CS 770
|
Topics in Artificial Intelligence | |
|
DSCI 691
|
Natural Language Processing with Deep Learning | |
|
INFO 629
|
Applied Artificial Intelligence | |
|
INFO 693
|
Human–Artificial Intelligence Interaction | |
|
BMES 547
|
Machine Learning in Biomedical Applications | |
|
ECE 612
|
Applied Machine Learning Engineering | |
|
ECE 613
|
Neuromorphic Computing | |
| Electives | 21.0 | |
| The remaining 7 courses may be selected from any focal area listed above or any graduate course in CCI (CI, CS, CT, SE, DSCI, INFO). For the ComputationalAIML concentration, at least two (2) of these must be within the CS department. | ||
| Up to two (2) of these may be approved independent studies. | ||
| Total Credits | 45.0 | |
Admissions Criteria
- A four-year bachelor's or master’s degree from a regionally accredited institution in Computer Science, Software Engineering, or related STEM degree, plus work experience equal to Drexel's Graduate Certificate in Computer Science
- Those without the above will have to complete the Graduate Certificate in Computer Science program (with grade B or better in each course) prior to admission to the master’s degree
- GPA of 3.0 or higher, in a completed degree program, bachelor’s degree or above
Required Documents
With multiple ways to submit documents, Drexel makes it easy to complete your application. Learn more by visiting our Completing Your Application Guide.
- A completed application
- Official transcripts from all universities or colleges and other post-secondary educational institutions attended (including trade schools)
- One letter of recommendation required, two suggested
- Essay/Statement of Purpose
- Resume
- Graduate Record Examination (GRE) Scores (five years old or less) are recommended, but not required for international students and domestic students with a GPA below 3.0
- Additional requirements for International Students
Computer Requirements
You must have access to a computer that meets or exceeds the minimum configuration outlined in the College of Computing and Informatics's Computer and Technology Requirements Guide.
Tuition
The tuition rate for the academic year 2023-2024 is $1396 per credit.
- This program is eligible for Financial Aid.
- Special tuition rates available for Drexel University Alumni, Military members, and members of our Partner Organizations
- These rates apply only to new online students and students being readmitted.
- Tuition rates are subject to increase with the start of each academic year in the fall term.
- All students must contact applyDUonline@drexel.edu within the first two weeks of the term to request tuition savings for which they qualify.
- Special rates cannot be combined. If you qualify for more than one special rate, you'll be given the one with the largest savings.
- When receiving special tuition plans with Drexel University Online, you may not combine them with other tuition benefits that may be available from Drexel University.
Academic Calendar
2022-2023 Academic Year
Term
Classes Begin
Classes End
Exams Begin
Exams End
Fall 2022
September 19, 2022
December 3, 2022
December 5, 2022
December 10, 2022
Winter 2023
January 9, 2023
March 18, 2023
March 20, 2023
March 25, 2023
Spring 2023
April 3, 2023
June 10, 2023
June 12, 2023
June 17, 2023
Summer 2023
June 26, 2023
September 2, 2023
September 4, 2023
September 9, 2023
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