Program Overview
College of Computing & Informatics- Location: Philadephia, PA
- Length: 24 months
- Tuition: $64,710
- Website: https://drexel.edu/cci/academics/masters-programs/ms-in-artificial-intelligence-machine-learning/
Drexel University’s Master of Science in Artificial Intelligence and Machine Learning (MSAIML) is a 45-credit graduate program designed to prepare students for careers in AI and machine learning. Offered both online and on-campus, the program provides flexibility for working professionals and full-time students alike.
Duration and Structure
Students can complete the program in 2–3 years full-time or 2–4 years part-time. Drexel operates on a quarter system with four 10-week terms per year, enabling more frequent course offerings and curriculum customization.
Curriculum and Specializations
The curriculum includes five core courses, three focus-area electives, and seven general electives. Students can select either the traditional path or the modular certificate path, which awards stackable credentials.
Applied Concentration
CS 501 Introduction to Programming
Introduces foundational programming skills for students with little or no prior experience. Prepares learners for advanced study in computer science through hands-on coding practice and core programming concepts.
CS 570 Programming Foundations
Covers essential programming principles to bring beginners and those with limited experience up to graduate-level readiness in computer science.
CS 614 Applications of Machine Learning
Explores the practical use of machine learning across domains such as vision, natural language, recommendation systems, and healthcare. Emphasizes selecting and applying ML methods to solve real-world problems.
INFO 629 Applied Artificial Intelligence
Focuses on selecting and applying AI methods like neural networks and deep learning to real-world tasks. Teaches how to match problems with AI techniques and evaluate their effectiveness.
CS 502 Data Structures and Algorithms
Covers core algorithmic techniques such as sorting, searching, and graph traversal. Includes analysis and implementation of data structures like trees and graphs.
CS 503 Systems Basics
Introduces operating systems and computer architecture fundamentals. Emphasizes Unix-based system tools, process management, memory, concurrency, and networking.
DSCI 501 Quantitative Foundations of Data Science
Teaches core mathematical concepts—linear algebra, calculus, probability, and statistics—used in data science. Integrates Python for practical, computational applications.
DSCI 511 Data Acquisition and Pre-Processing
Covers early stages of the data science lifecycle, including data collection, cleaning, and curation. Includes a term project to apply these skills using real data.
DSCI 521 Data Analysis and Interpretation
Focuses on statistical and algorithmic techniques to explore and analyze pre-processed data. Includes hands-on projects involving hypothesis testing and pattern detection.
DSCI 631 Applied Machine Learning for Data Science
Covers the full machine learning workflow, from feature engineering to model evaluation. Offers exposure to various algorithms and emphasizes practical application through projects.
INFO 612 Knowledge-based Systems
Examines how knowledge is represented and used in AI. Covers topics such as ontologies, reasoning, cognitive systems, and the integration of symbolic and data-driven methods.
INFO 692 Explainable Artificial Intelligence
Explores techniques that make ML models more interpretable. Focuses on explanation methods that help users understand model decisions, performance, and limitations.
INFO 693 Human–Artificial Intelligence Interaction
Analyzes how humans interact with AI systems. Covers UX, algorithmic fairness, and AI ethics. Emphasizes design and critical thinking without requiring programming experience.
More information here:
Computational Concentration
CS 510 Introduction to Artificial Intelligence
Covers core AI concepts such as search algorithms, logic-based reasoning, knowledge representation, and planning techniques. Includes an introduction to Lisp and functional programming.
CS 613 Machine Learning
Focuses on Bayesian modeling and statistical learning techniques. Topics include classification, regression, clustering, hidden Markov models, and decision theory.
CS 615 Deep Learning
Teaches deep learning fundamentals and architectures such as CNNs and RNNs. Includes algorithm implementation, model evaluation, and applications in image, speech, and language processing.
CS 521 Data Structures and Algorithms I
Introduces key algorithmic techniques and data structures including hashing, trees, heaps, and graph traversal. Emphasizes analysis and practical applications.
CS 525 Theory of Computation
Explores foundational models of computation including finite automata, Turing machines, and context-free grammars. Covers concepts of decidability and computational complexity.
CS 583 Introduction to Computer Vision
Provides a foundation in computer vision, covering image processing, feature extraction, motion analysis, and object recognition through mathematical and computational models.
CS 589 Responsible Machine Learning
Addresses ethical concerns in ML applications. Focuses on fairness, transparency, and accountability in model design and deployment.
CS 610 Advanced Artificial Intelligence
Covers decision-making under uncertainty, learning with large datasets, and time-dependent problem solving. Emphasizes applying AI techniques in dynamic environments.
CS 611 Game Artificial Intelligence
Examines AI in game design, including movement, pathfinding, decision-making, and strategy. Balances technical implementation with gameplay experience.
CS 614 Applications of Machine Learning
Explores practical applications of ML in domains like natural language, vision, and healthcare. Emphasizes method selection based on domain-specific requirements.
CS 616 Robust Deep Learning
Focuses on deep learning system security, including adversarial attacks and defenses. Applies theory to tasks like object detection and NLP through hands-on projects.
CS 618 Algorithmic Game Theory
Introduces algorithmic solutions to game-theoretic problems. Topics include auctions, fair division, online markets, and incentive-driven behavior modeling.
CS 630 Cognitive Systems
Investigates computational models that emulate human cognition. Reviews modeling frameworks and real-world applications in cognitive computing.
DSCI 691 Natural Language Processing with Deep Learning
Covers state-of-the-art neural network approaches in NLP. Emphasizes research-based learning and hands-on experience with language models.
INFO 629 Applied Artificial Intelligence
Teaches how to apply AI algorithms to real-world problems. Covers neural networks, search methods, and expert systems in various domains like healthcare and autonomous vehicles.
More information here:
The program concludes with a two-term capstone project involving real-world or research-based AI/ML challenges.
Tuition and Cost
Tuition is $1,438 per credit, bringing the estimated total cost to approximately $64,710 before fees and potential financial aid. Scholarships and graduate co-op opportunities are available for eligible students.
More tuition information available here: https://drexel.edu/drexelcentral/cost/tuition/graduate/
Admission Requirements
Applicants need a bachelor’s degree in computer science or a related STEM field for the computational track. Students without a technical background may be required to complete a foundational certificate before entering the degree program. The GRE is recommended for those with a GPA under 3.0.
Online and On-Campus Options
The program is available fully online or on-campus, giving students the choice to pursue their education from anywhere. On-campus students also have access to co-op placements through Drexel’s Steinbright Career Development Center.
Outcomes and Career Fit
Graduates are equipped with skills in algorithms, data science, applied machine learning, and AI systems. This program is ideal for professionals seeking career growth in data-driven industries or transitioning into AI/ML roles from adjacent technical fields.
1. Technical Professionals Seeking Specialization
Those with a background in computer science, data science, software engineering, or other STEM disciplines who want to specialize in AI and machine learning.
2. Mid-Career Technologists Looking to Advance
Professionals already working in tech roles (e.g., software developers, data analysts, systems engineers) who want to move into more advanced AI/ML positions or leadership roles in AI-driven projects.
3. Career Changers from Related Fields
Individuals from adjacent fields like statistics, engineering, or mathematics who want to pivot into artificial intelligence and machine learning, especially those willing to complete a foundational certificate if needed.
4. Professionals Needing Flexibility
Working adults seeking part-time or online learning options with the flexibility to balance studies with personal and professional responsibilities.
5. International Students in STEM Fields
Students from abroad interested in a STEM-designated degree program that qualifies for up to 3 years of OPT (Optional Practical Training) in the U.S.
6. Lifelong Learners Focused on Real-World Application
Learners who value applied learning and want hands-on experience solving real problems via capstone projects and possibly co-op placements.
Conclusion
With a rigorous curriculum, flexible format, and applied learning approach, Drexel’s MSAIML program prepares students for high-demand careers in AI and machine learning across various sectors.