Artificial Intelligence & Machine Learning
Major: Artificial Intelligence & Machine Learning
Degree Awarded: Bachelor of Science (BS)
Calendar Type: Quarter
Minimum Required Credits: 180.0
Co-op Options: Three Co-op (Five years); One Co-op (Four years)
Classification of Instructional Programs (CIP) code: 11.0701
Standard Occupational Classification 15-0000
About the Program
Note: Students will be accepted into this program beginning Fall 2025 for a Fall 2026 admittance. For more information or to apply, please go to Undergraduate Application Instructions.
The College of Computing & Informatics' Bachelor of Science in Artificial Intelligence & Machine Learning (AIML) provides a strong foundation in these areas, combining conceptual and theoretical knowledge with hands-on practice and applications. The program is designed for maximum flexibility, allowing students to tailor their study of AI and machine learning along specific focus areas (e.g., theory, data analytics, hardware, and/or practical applications). Through coursework and possibly double majors, students can also blend their study with a wide variety of other fields, including computing, physical or social sciences, engineering, and arts and humanities. The hands-on curriculum combined with co-op provides real-world experience that culminates in a full-year team capstone project involving in-depth study and application of computing and informatics. Graduates of the BS AIML program are in high demand in a vast array of industries where knowledge of AI and machine learning is critical for success.
Additional Information
For more information about this program, please visit the Undergraduate Programs page on the College of Computing & Informatics website.
Degree Requirements
Core Requirements | ||
CS 171 | Computer Programming I | 3.0 |
or ENGR 131 | Introductory Programming for Engineers | |
or ENGR 132 | Programming for Engineers | |
CS 172 | Computer Programming II | 3.0 |
or ECE 105 | Programming for Engineers II | |
CS 180 | Introduction to Artificial Intelligence & Machine Learning | 3.0 |
CS 260 | Data Structures | 4.0 |
CS 265 | Advanced Programming Tools and Techniques | 3.0 |
CS 380 | Artificial Intelligence | 3.0 |
CS 383 | Machine Learning | 3.0 |
or INFO 213 | Data Science Programming II | |
or ECE 310 | Machine Learning Engineering Practicum | |
INFO 212 | Data Science Programming I | 3.0 |
INFO 215 | Social Aspects of Information Systems | 3.0 |
SE 201 | Introduction to Software Engineering and Development | 3.0 |
SE 310 | Software Design | 3.0 |
Elective Requirements | ||
Select 8 courses from the list below. For sequences, each course may count separately toward this total provided that it is not already used to satisfy another degree requirement. | 24.0 | |
Systems Architecture and Systems Programming and High Performance Computing * | ||
Evolutionary Computing | ||
Game AI Development | ||
Reinforcement Learning | ||
or ECE 417 | Reinforcement Learning | |
Computational Photography | ||
Computational Network Neuroscience | ||
Advanced Artificial Intelligence | ||
Robust Machine Learning | ||
Topics in Artificial Intelligence | ||
Digital Logic Design and Introduction to Computer Organization and Neuromorphic Computing | ||
Introduction to Computer Organization and High Performance Computing * | ||
Introduction to Computer Organization and Discrete Mathematics and Intelligent System Architectures | ||
Introduction to Multimedia Forensics and Security | ||
Cell and Tissue Image Analysis | ||
Pattern Recognition | ||
Bioinformatics | ||
Statistical Analysis of Metagenomics | ||
Recommender Systems | ||
Applied Deep Learning | ||
Database Management Systems and Cloud Computing and Big Data | ||
Information Retrieval Systems | ||
Human-Centered Design Process & Methods | ||
Exploratory Data Analytics | ||
Data Mining Applications | ||
Advanced Data Analytics | ||
Social Media Data Analysis | ||
Project Requirements | ||
Select one of the following sequences: | 6.0 | |
Computing and Informatics Design I and Computing and Informatics Design II and Computing and Informatics Design III | ||
Introduction to Engineering Design & Data Analysis and First-Year Engineering Design | ||
Choose one of the following sequences: | 9.0 | |
Senior Project I and Senior Project II and Senior Project III | ||
Senior Design Project I and Senior Design Project II and Senior Design Project III | ||
Mathematics Requirements | ||
Select one of the following sequences: | 11.0-14.0 | |
Algebra, Functions, and Trigonometry and Calculus I and Calculus II | ||
Calculus and Functions I and Calculus and Functions II and Calculus II | ||
Calculus I and Calculus II and Multivariate Calculus ** | ||
MATH 201 | Linear Algebra | 3.0-4.0 |
or ECE 231 | Linear Algebra and Matrix Computations | |
or ENGR 231 | Linear Engineering Systems | |
MATH 311 | Probability and Statistics I | 4.0 |
or ECE 361 | Probability and Data Analytics for Engineers | |
Science Requirements | ||
Select from the following 100-400 level courses: | 20.0 | |
Any BMES (Biomedical Engineering & Science) | ||
Any BIO (Bioscience & Biotechnology) | ||
Any CHEM (Chemistry) | ||
Any ENVS (Environmental Science) | ||
Any GEO (Geoscience) | ||
Any PHEV (Physics-Environmental Science) | ||
Any PHYS (Physics) | ||
Arts & Humanities Requirements | ||
COM 230 | Techniques of Speaking | 3.0 |
or COM 310 | Technical Communication | |
ENGL 101 | Composition and Rhetoric I: Inquiry and Exploratory Research | 3.0 |
or ENGL 111 | English Composition I | |
ENGL 102 | Composition and Rhetoric II: Advanced Research and Evidence-Based Writing | 3.0 |
or ENGL 112 | English Composition II | |
ENGL 103 | Composition and Rhetoric III: Themes and Genres | 3.0 |
or ENGL 113 | English Composition III | |
PHIL 311 | Ethics and Information Technology | 3.0 |
University Requirements | ||
UNIV CI101 | The Drexel Experience | 2.0 |
or CI 120 | CCI Transfer Student Seminar | |
CIVC 101 | Introduction to Civic Engagement | 1.0 |
COOP 101 | Career Management and Professional Development | 1.0 |
Free Electives | ||
Select any unrestricted 099-499 courses | 46.0-50.0 | |
Total Credits | 180.0 |
- *
Only one of the two High Performance Computing sequences may be selected.
- **
Instead of MATH 200, students may substitute CS 270, ECE 232, ENGR 232, or any other MATH course not used to fulfill another requirement.
Writing-Intensive Course 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.
A "WI" next to a course in this catalog may indicate that this course can fulfill a writing-intensive requirement. For the most up-to-date list of writing-intensive courses being offered, students should check the Writing Intensive Course List at the University Writing Program. Students scheduling their courses can also conduct a search for courses with the attribute "WI" to bring up a list of all writing-intensive courses available that term.
Sample Plan of Study
5-year, 3 co-op
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CI 101 | 2.0 | CI 102 | 2.0 | CI 103 | 2.0 | Vacation | |
CS 180 | 3.0 | CIVC 101 | 1.0 | COOP 101 | 1.0 | ||
ENGL 101 or 111 | 3.0 | CS 171 | 3.0 | CS 172 | 3.0 | ||
MATH 121 | 4.0 | ENGL 102 or 112 | 3.0 | ENGL 103 or 113 | 3.0 | ||
UNIV CI101 | 1.0 | MATH 122 | 4.0 | MATH 200 | 4.0 | ||
Free Elective | 3.0 | Science Elective | 4.0 | UNIV CI101 | 1.0 | ||
Science Elective | 4.0 | ||||||
16 | 17 | 18 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CS 265 | 3.0 | CS 260 | 4.0 | Co-op Experience | Co-op Experience | ||
INFO 212 | 3.0 | INFO 215 | 3.0 | ||||
MATH 201 | 4.0 | MATH 311 | 4.0 | ||||
SE 201 | 3.0 | Science Elective | 4.0 | ||||
Science Elective | 4.0 | Free Elective | 3.0 | ||||
17 | 18 | 0 | 0 | ||||
Third Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
COM 230 | 3.0 | CS 383 | 3.0 | Co-op Experience | Co-op Experience | ||
CS 380 | 3.0 | AI/ML Elective | 3.0 | ||||
SE 310 | 3.0 | Science Elective | 4.0 | ||||
AI/ML Elective | 3.0 | Free Electives | 6.0 | ||||
Free Elective | 3.0 | ||||||
15 | 16 | 0 | 0 | ||||
Fourth Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
PHIL 311 | 3.0 | AI/ML Electives | 6.0 | Co-op Experience | Co-op Experience | ||
AI/ML Electives | 6.0 | Free Electives | 6.0 | ||||
Free Electives | 6.0 | ||||||
15 | 12 | 0 | 0 | ||||
Fifth Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
CI 491 | 3.0 | CI 492 | 3.0 | CI 493 | 3.0 | ||
AI/ML Elective | 3.0 | AI/ML Elective | 3.0 | Free Electives | 9.0 | ||
Free Electives | 6.0 | Free Electives | 6.0 | ||||
12 | 12 | 12 | |||||
Total Credits 180 |
4-year, 1 co-op
First Year | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CI 101 | 2.0 | CI 102 | 2.0 | CI 103 | 2.0 | Vacation | |
CS 180 | 3.0 | CIVC 101 | 1.0 | CS 172 | 3.0 | ||
ENGL 101 or 111 | 3.0 | CS 171 | 3.0 | ENGL 103 or 113 | 3.0 | ||
MATH 121 | 4.0 | ENGL 102 or 112 | 3.0 | MATH 200 | 4.0 | ||
UNIV CI101 | 1.0 | MATH 122 | 4.0 | UNIV CI101 | 1.0 | ||
Free Elective | 3.0 | Science Elective | 4.0 | Science Elective | 4.0 | ||
16 | 17 | 17 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CS 265 | 3.0 | CS 260 | 4.0 | COM 230 | 3.0 | COOP 101 | 1.0 |
INFO 212 | 3.0 | INFO 215 | 3.0 | CS 380 | 3.0 | CS 383 | 3.0 |
MATH 201 | 4.0 | MATH 311 | 4.0 | SE 310 | 3.0 | AI/ML Elective | 3.0 |
SE 201 | 3.0 | Science Elective | 4.0 | AI/ML Elective | 3.0 | Science Elective | 4.0 |
Science Elective | 4.0 | Free Elective | 3.0 | Free Elective | 3.0 | Free Electives | 6.0 |
17 | 18 | 15 | 17 | ||||
Third Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
PHIL 311 | 3.0 | AI/ML Electives | 6.0 | Co-op Experience | Co-op Experience | ||
AI/ML Electives | 6.0 | Free Electives | 6.0 | ||||
Free Electives | 6.0 | ||||||
15 | 12 | 0 | 0 | ||||
Fourth Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
CI 491 | 3.0 | CI 492 | 3.0 | CI 493 | 3.0 | ||
AI/ML Elective | 3.0 | AI/ML Elective | 3.0 | Free Electives | 9.0 | ||
Free Electives | 6.0 | Free Electives | 6.0 | ||||
12 | 12 | 12 | |||||
Total Credits 180 |
Co-op/Career Opportunities
Co-Op Options
Two co-op options are available for this program:
- five-year/three co-op
- four-year/one co-op
Career Opportunities
The demand for computing skills is tremendous and growing with highly paid jobs. Most professionals in the field focus on the design and development of artificial intelligence- and machine learning-centered applications. Typical jobs include software engineer, programmer, data scientist, systems analyst or consultant, and manager of technical staff. Most positions require at least a bachelor’s degree. Relevant work experience, such as that provided by co-operative education, is also very important, as cited by the Occupational Outlook Handbook published by the US Bureau of Labor Statistics.
Additional Information
Visit the Drexel Steinbright Career Development Center page for more detailed information on co-op and post-graduate opportunities.
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.
Program Level Outcomes
The College of Computing & Informatics works continually to improve its degree programs. As part of this effort, the Artificial Intelligence & Machine Learning degree is evaluated relative to the following Objectives and Outcomes:
Artificial Intelligence & Machine Learning Program Educational Objectives
Drexel Artificial Intelligence & Machine Learning alumni will:
- Be valued employees in a wide variety of occupations in industry, government and academia, in particular as artificial intelligence and machine learning scientists and engineers
- Succeed in graduate and professional studies, such as engineering, science, law, medicine, and business
- Pursue life-long learning and professional development to remain current in an ever-changing technological world
- Provide leadership in their profession, in their communities, and society
- Function as responsible members of society with an awareness of the social and ethical ramifications of their work
Artificial Intelligence & Machine Learning Student Outcomes
The Drexel Artificial Intelligence & Machine Learning program enables students to attain by the time of graduation:
- An ability to analyze a problem and identify and define the use of artificial intelligence and/or machine learning (AI/ML) as appropriate to its solution
- An ability to interpret and communicate the output of statistical and algorithmic methods
- An ability to function effectively on a team to design and implement a computer-based AI/ML system
- An ability to understand the implementation and use of AI/ML tools and systems
- An ability to apply mathematical foundations, algorithmic principles, and computational knowledge in the modeling and design of AI/ML systems in a way that demonstrates comprehension of the tradeoffs involved in design choices
- An ability to design, implement, and evaluate a computer-based AI/ML system, process, component, or program to meet desired needs
- An ability to apply sound software engineering principles in the construction of computer-based AI/ML systems of varying complexity
- An ability to understand and communicate the ethical aspects of AI/ML, and to communicate these aspects as part of result interpretation
- An ability to understand and communicate the legal and ethical aspects of using AI/ML in societal contexts