Program Overview

Master of Science in Artificial Intelligence & Machine Learning

The Master of Science 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, students will 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.

Program Overview

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.

Program Objectives

The MS in Artificial Intelligence & Machine Learning will prepare students 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

Curriculum

The program is organized into four 10-week quarters per year. One semester credit is equivalent to 1.5 quarter credits.

Core Courses

• Choose appropriate core courses for concentration: 9.0 credits
  • 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

• Choose five courses with at least one course from each group, for the appropriate concentration: 15.0 credits
  • 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 for Data Science
      • 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 for Data Science
      • 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

• Choose 5 additional courses, which may include: 15.0 credits
  • 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 credits
• CS 592: Artificial Intelligence and Machine Learning Capstone II: 3.0 credits

Total Credits

• 45.0-46.0 credits

Admissions Criteria

• Applied Concentration
  • A four-year bachelor's degree from a regionally accredited institution in the United States or an equivalent international institution
• Computational Concentration
  • 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 Foundations
• GPA of 3.0 or higher, in a completed degree program, bachelor's degree or above

Required Documents

• 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 (academic, professional, or both)
• Essay/Statement of Purpose: In approximately 500 words, describe what professional goals you hope to achieve, how an advanced degree facilitates that success and anything else you want the Admissions Review Board to know about you
• Resume
• Graduate Record Examination (GRE) scores are not required. They are highly recommended for international applicants and for domestic applications with a GPA below 3.0 in their undergraduate degree.
• 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 is $1481 per credit. For the academic year, students enrolled in an online graduate academic program will be charged a graduate online program fee of $125 per year.

Academic Calendar

• Fall 2025
  • Classes Begin: September 22, 2025
  • Classes End: December 6, 2025
  • Exams Begin: December 8, 2025
  • Exams End: December 13, 2025
• Winter 2026
  • Classes Begin: January 5, 2026
  • Classes End: March 14, 2026
  • Exams Begin: March 16, 2026
  • Exams End: March 21, 2026
• Spring 2026
  • Classes Begin: March 30, 2026
  • Classes End: June 6, 2026
  • Exams Begin: June 8, 2026
  • Exams End: June 13, 2026
• Summer 2026
  • Classes Begin: June 22, 2026
  • Classes End: August 29, 2026
  • Exams Begin: August 31, 2026
  • Exams End: September 5, 2026

Career Opportunities

Common jobs within the industry include:

• Data Scientist
• Software Engineer
• Deep Learning Engineer
• Algorithm Developer
• Computer Vision Engineer

Salary Information

• 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

Program Duration

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.