Artificial Intelligence and Machine Learning MSAIML < Drexel University Catalog

Artificial Intelligence and Machine Learning MSAIML < Drexel University Catalog
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> Artificial Intelligence and Machine Learning MSAIML
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
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
Major Specific Electives
15.0
Choose five courses with at least one course 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
DSCI 641
Recommender Systems
INFO 623
Social Network Analytics
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
DSCI 691
Natural Language Processing with Deep Learning
INFO 612
Knowledge-based Systems
INFO 692
Explainable Artificial Intelligence
Human-Centered Computing
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
INFO 693
Human–Artificial Intelligence Interaction
INFO 725
Information Policy and Ethics
Computational
Data Science and Analytics
CS 660
Data Analysis at Scale
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 623
Social Network Analytics
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 540
High Performance Computing
CS 567
Applied Symbolic Computation
CS 616
Robust Deep Learning
CS 770
Topics in Artificial Intelligence
ECES 521
Probability & Random Variables
MATH 504
Linear Algebra & Matrix Analysis
MATH 510
Applied Probability and Statistics I
Applications of AI/ML
CS 583
Introduction to Computer Vision
CS 589
Responsible Machine Learning
CS 610
Advanced Artificial Intelligence
CS 611
Game Artificial Intelligence
CS 614
Applications of Machine Learning
CS 618
Algorithmic Game Theory
CS 630
Cognitive Systems
DSCI 641
Recommender Systems
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
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
COOP 500
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
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
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
Second Year
Fall
Credits
Winter
Credits
Spring
Credits
COOP EXPERIENCE
COOP EXPERIENCE
CS 592
3.0
Flexible Elective
6.0
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
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Writing-intensive Requirements
In order to graduate, all students must pass three writing-intensive courses after their freshman year. Two writing-intensive courses must be in a student's major. The third can be in any discipline. Students are advised to take one writing-intensive class each year, beginning with the sophomore year, and to avoid “clustering” these courses near the end of their matriculation. Transfer students need to meet with an academic advisor to review the number of writing-intensive courses required to graduate.
For additional information, and an up-to-date list of the writing-intensive courses being offered, students should check the Drexel University Writing Center page