Artificial Intelligence and Machine Learning MSAIML
Major: Artificial Intelligence and Machine Learning
Degree Awarded: Master of Science in Artificial Intelligence and Machine Learning (MSAIML)
Calendar Type: Quarter
Minimum Required Credits: 45.0-46.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 11.0701
Standard Occupational Classification (SOC) code: 15-0000
About the Program
The Master of Science in Artificial Intelligence and Machine Learning provides a strong foundation in the artificial intelligence and machine learning fields with foci on mathematical foundations, algorithms, tools, and applications as they pertain to artificial intelligence and machine learning. Students will pursue an applied or computational track and will gain competency in fundamental methods and techniques in artificial intelligence and machine learning. Their fundamental understanding will be applied to real data sets and data analysis tasks with the help of state-of-the-art technologies, tools, and platforms. The Master of Science in Artificial Intelligence and Machine Learning program culminates with a two-term capstone experience where students work on a real world or research problem using the knowledge they have gained throughout the program.
Note that this degree has two concentrations available: computational and applied. Please refer to the College of Computing & Informatics website for complete information.
A graduate co-op is available; for more information, visit the Steinbright Career Development Center's website.
Admission Requirements
The Master of Science in Artificial Intelligence and Machine Learning accepts applicants who hold a four-year bachelor's degree 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 Post-Baccalaureate Certificate in Computer Science Foundations. Please visit the College of Computing & Informatics website for more information on admission requirements.
Additional Information
For more information about this program, visit the College of Computing & Informatics MS in Artificial Intelligence and Machine Learning webpage.
Degree Requirements
Core Courses | ||
Choose appropriate core courses for concentration: | 9.0 | |
Applied | ||
Introduction to Programming | ||
or CS 570 | Programming Foundations | |
Applications of Machine Learning | ||
Applied Artificial Intelligence | ||
Computational | ||
Introduction to Artificial Intelligence | ||
Machine Learning | ||
Deep Learning | ||
Major Specific Electives | 15.0 | |
Choose five courses with at least one course from each group, for the appropriate concentration. | ||
Applied | ||
Data Science Foundations | ||
Quantitative Foundations of Data Science | ||
Data Acquisition and Pre-Processing | ||
Data Analysis and Interpretation | ||
Applied Machine Learning for Data Science | ||
Applied Cloud Computing | ||
Data Analytics for Community-Based Data and Service | ||
Social Network Analytics | ||
AI Foundations | ||
Data Structures and Algorithms | ||
Systems Basics | ||
Introduction to Artificial Intelligence | ||
Machine Learning | ||
Knowledge-based Systems | ||
Explainable Artificial Intelligence | ||
Human-Centered Computing | ||
Security, Policy and Governance | ||
Information Innovation through Design Thinking | ||
Foundations of Data and Information | ||
Human-Computer Interaction | ||
Human–Artificial Intelligence Interaction | ||
Information Policy and Ethics | ||
Computational * | ||
Data Science and Analytics | ||
Data Analysis at Scale | ||
Quantitative Foundations of Data Science | ||
Data Acquisition and Pre-Processing | ||
Data Analysis and Interpretation | ||
Applied Machine Learning for Data Science | ||
Applied Cloud Computing | ||
Data Analytics for Community-Based Data and Service | ||
Social Network Analytics | ||
Algorithmic Foundations | ||
Data Structures and Algorithms I | ||
Data Structures and Algorithms II | ||
Theory of Computation | ||
High Performance Computing | ||
Applied Symbolic Computation | ||
Topics in Artificial Intelligence | ||
Probability & Random Variables | ||
Linear Algebra & Matrix Analysis | ||
Applied Probability and Statistics I | ||
Applications of AI/ML | ||
Applications of Machine Learning | ||
Introduction to Computer Vision | ||
Advanced Artificial Intelligence | ||
Game Artificial Intelligence | ||
Algorithmic Game Theory | ||
Cognitive Systems | ||
Natural Language Processing with Deep Learning | ||
Applied Artificial Intelligence | ||
Human–Artificial Intelligence Interaction | ||
Machine Learning in Biomedical Applications | ||
Applied Machine Learning Engineering | ||
Neuromorphic Computing | ||
Flexible Electives | 15.0 | |
Choose 5 additional courses, which may include: | ||
Any graduate-level courses within the College (CI, CS, CT, DSCI, INFO, SE) | ||
Up to 6 credits of independent study | ||
Up to 6 credits of related graduate-level coursework outside of the College, with prior approval by the College | ||
Capstone Courses | ||
CS 591 | Artificial Intelligence and Machine Learning Capstone I | 3.0 |
CS 592 | Artificial Intelligence and Machine Learning Capstone II | 3.0 |
Optional Coop Experience | 0-1 | |
Career Management and Professional Development for Master's Degree Students * | ||
Total Credits | 45.0-46.0 |
- *
For the Computational concentration, at least 2 of these courses must be CS courses.
- **
Co-op is an option for this degree for full-time on-campus students. To prepare for the 6-month co-op experience, students will complete: COOP 500. The total credits required for this degree with the co-op experience is 46.0.
Students not participating in the co-op experience will need 45.0 credits to graduate.
Sample Plan of Study
Part time, No co-op
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
Core Courses | 6.0 | Core Course | 3.0 | Major Specific Electives | 6.0 | Major Specific Elective | 6.0 |
Major Specific Elective | 3.0 | ||||||
6 | 6 | 6 | 6 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
Flexible Electives | 6.0 | Flexible Electives | 6.0 | CS 591 | 3.0 | CS 592 | 3.0 |
Flexible Elective | 3.0 | ||||||
6 | 6 | 6 | 3 | ||||
Total Credits 45 |
Note: Second Year Summer is less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students this term.
Full time, With Co-op
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
Core Courses | 6.0 | Core Course | 3.0 | Major Specific Electives | 6.0 | COOP EXPERIENCE | |
Major Specific Electives | 3.0 | Major Specific Electives | 6.0 | Flexible Elective | 3.0 | ||
COOP 500 | 1.0 | ||||||
10 | 9 | 9 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
COOP EXPERIENCE | CS 591 | 3.0 | CS 592 | 3.0 | |||
Flexible Electives | 6.0 | Flexible Electives | 6.0 | ||||
0 | 9 | 9 | |||||
Total Credits 46 |
3675 Market Street
The College of Computing & Informatics is located at 3675 Market. Occupying three floors in the modern uCity Square building, CCI's home offers state-of-the-art technology in our classrooms, research labs, offices, meeting areas and collaboration spaces. 3675 Market offers Class A laboratory, office, coworking, and convening spaces. Located at the intersection of Market Street and 37th Street, 3675 Market acts as a physical nexus, bridging academic campuses and medical centers to the east and south, the commercial corridors along Market Street and Chestnut Street, and the residential communities to the north and west.
The uCity Square building offers:
- Speculative lab/office space
- World-class facilities operated by CIC
- Café/restaurant on-site
- Quorum, a two-story, 15K SF convening space and conference center
- Adjacent to future public square
- Access to Science Center’s nationally renowned business acceleration and technology commercialization programs
Drexel University Libraries
Drexel University Libraries is a learning enterprise, advancing the University’s academic mission through serving as educators, supporting education and research, collaborating with researchers, and fostering intentional learning outside of the classroom. Drexel University Libraries engages with Drexel communities through three physical locations, including W. W. Hagerty Library, Queen Lane Library, and the Library Learning Terrace, as well as a vibrant online presence which sees, on average, over 8,000 visits per day. In the W.W. Hagerty Library location, College of Computing & Informatics students have access to private study rooms and nearly half a million books, periodicals, DVDs, videos and University Archives. All fields of inquiry are covered, including: library and information science, computer science, software engineering, health informatics, information systems, and computing technology. Resources are available online at library.drexel.edu or in-person at W. W. Hagerty Library.
The Libraries also make available laptop and desktop PC and Mac computers, printers and scanners, spaces for quiet work or group projects and designated 24/7 spaces. Librarians and library staff—including a liaison librarian for computing and informatics—are available for individual research consultations and to answer questions about materials or services.
CCI Commons
Located on the 10th floor of 3675 Market Street, the CCI Commons is an open lab and collaborative work environment for students. It features desktop computers, a wireless/laptop area, free black and white printing, and more collaborative space for its students. Students have access to 3675 Market's fully equipped conference room with 42” displays and videoconferencing capabilities. The CCI Commons provides technical support to students, faculty, and professional staff. In addition, the staff provides audio-visual support for all presentation classrooms within 3675 Market. Use of the CCI Commons is reserved for all students taking CCI courses.
The computers for general use are Microsoft Windows and Macintosh OSX machines with appropriate applications which include the Microsoft Office suite, various database management systems, modeling tools, and statistical analysis software. Library-related resources may be accessed at the CCI Commons and through the W.W. Hagerty Library. The College is a member of the "Azure Dev Tools for Teaching” platform that allows students free access to a wide array of Microsoft software titles and operating systems.
The CCI Commons, student labs, and classrooms have access to networked databases, print and file resources within the College, and the Internet via the University’s network. Email accounts, Internet and BannerWeb access are available through the Office of Information Resources and Technology.
CCI Learning Center
The CCI Learning Center (CLC), located in 3675 Market Street's CCI Commons student computer lab, provides consulting and other learning resources for students taking courses offered by the Computer Science Department. The CLC is staffed by graduate and undergraduate computer science students from the College of Computing & Informatics.
The CLC and CCI Commons serve as a central hub for small group work, student meetings, and TA assistance.
Research Laboratories
The College houses multiple research labs, led by CCI faculty, in 3675 Market Street including: the Metadata Research Center (MRC), Interactive Systems for Healthcare (IS4H) Research, Economics and Computation (EconCS), The TeX-Base Lab, SPiking And Recurrent SoftwarE (SPARSE) Coding, Human-System Evaluation and Analysis Lab (H-SEAL), Applied Symbolic Computation Laboratory (ASYM), Software Engineering Research Group (SERG), Social Computing Research Group, Vision and Cognition Laboratory (VisCog) and the Vision and Graphics Laboratory. For more information on these laboratories, please visit the College’s research web page.