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 | ||
Recommender Systems for Data Science | ||
Social Network Analytics | ||
Introduction to Data Analytics | ||
AI Foundations | ||
Data Structures and Algorithms | ||
Systems Basics | ||
Introduction to Artificial Intelligence | ||
Machine Learning | ||
Natural Language Processing with Deep 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 | ||
Social Network Analytics | ||
Introduction to Data Analytics | ||
Algorithmic Foundations | ||
Data Structures and Algorithms I | ||
Data Structures and Algorithms II | ||
Theory of Computation | ||
High Performance Computing | ||
Applied Symbolic Computation | ||
Robust Deep Learning | ||
Topics in Artificial Intelligence | ||
Probability & Random Variables | ||
Linear Algebra & Matrix Analysis | ||
Applied Probability and Statistics I | ||
Applications of AI/ML | ||
Introduction to Computer Vision | ||
Responsible Machine Learning | ||
Advanced Artificial Intelligence | ||
Game Artificial Intelligence | ||
Applications of Machine Learning | ||
Algorithmic Game Theory | ||
Cognitive Systems | ||
Recommender Systems for Data Science | ||
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 |
COOP 500 | 1.0 | Core Course | 3.0 | Major Specific Electives | 6.0 | Flexible Electives | 6.0 |
Core Courses | 6.0 | Major Specific Electives | 6.0 | Flexible Elective | 3.0 | CS 591 | 3.0 |
Major Specific Electives | 3.0 | ||||||
10 | 9 | 9 | 9 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
COOP EXPERIENCE | COOP EXPERIENCE | CS 592 | 3.0 | ||||
Flexible Elective | 6.0 | ||||||
0 | 0 | 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
The Drexel University Libraries is a one-stop resource for all members of the Drexel community, providing access to millions of print and online books, journals, databases and other media, as well as hundreds of online course and research guides, workshops, and tutorials. Expert librarians offer a variety of consultation services virtually or in person, including help with course-related projects, strategies for finding and evaluating authoritative information, and approaches to utilizing, organizing, and presenting scholarship.
Students in the College of Computing & Informatics also have access to the W. W. Hagerty Library where they can take advantage of the Libraries’ various learning environments, including group study rooms, collaborative and silent study areas, and 24/7 study space in the Dragons’ Learning Den. The Libraries also offers a wellness room, printing and scanning services, and laptops, portable power chargers, and other equipment you can borrow for use in the Library.
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.
Computer Support for Teaching
The CCI server room houses a multitude of servers to support faculty research, staff operations, and student learning. Services provided include a Linux compute cluster which is open to all faculty, staff, and students, multiple virtualization environments to meet different needs of faculty, staff, and students, and other single-purpose servers to support various operations throughout the college. The compute cluster provides a common environment for students to develop software, which makes testing easier for the TAs and faculty. Our virtualization environments allow college members the flexibility of a cloud environment with local support and direct cost recovery options. For those who need dedicated hardware, we also support dedicated research systems.
Classrooms are outfitted with laser projectors, 4K displays, class capture hardware, and the Wolfvision Cynap. The Cynap controls the AV distribution throughout the room and can display up to 4 streams simultaneously. These include the local PC, a laptop connected directly to the podium, or up to 4 streaming devices. Windows, macOS, iOS and Android devices can all connect wirelessly to the presentation system, allowing collaboration and freedom to roam the classroom for better interactivity. Wireless networking and outlets are also available for students throughout the classrooms. Laptops are available for checkout from the CCI Commons desk.
Additionally, CCI is hosting and supporting multiple Virtual Computing Lab environments for students to use that mimics the physical computer labs in CCI. This technology allows both online and face to face students to have the same experience when using computing facilities.
CCI Virtual Environments
CCI hosts a variety of virtual environments, which support all levels of research, academics, and administration at CCI. These include OpenStack, Proxmox VE, VMWare, and Xen architectures, backed by storage in CEPH. Multiple environments allow CCI IT to provide researchers with the level of control appropriate for the project at hand and make efficient use of project funding. External cloud vendors such as AWS and Google Cloud Platform are also used when appropriate.
CCI continues to invest in these virtual environments, and explores emerging environments, to continue to best support CCI research and teaching. CPU cores, storage, and memory are added at every opportunity to these flexible, scalable environments. The current capacity of the system includes:
- 1760 CPU Cores
- 6 TB of Memory
- Over 556 TB of HDD-backed storage
- 122 TB of high-performance SSD-backed storage
- 12 GPUs with room for expansion through funded research for high-performance computing needs
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), Security and Privacy Analytics Lab (SePAL), Software Engineering and Analytics Research (SOAR), Software Engineering Research Group (SERG), Social Computing Research Group, Vision and Cognition Laboratory (VisCog). For more information on these laboratories, please visit the College’s research web page.