Master of Science in Artificial Intelligence Curriculum
Required core courses
- Design and Analysis of Algorithms
- Principles of AI
- Machine Learning
Depth courses (at least 9 credits)
- Introduction to Machine Learning
- Computational Perception
- Introduction to Natural Language Processing
- Optimization for Machine Learning
- Introduction to Computer Vision
Advanced courses (at least 3 credits)
- Distributed Algorithms
- Parallel Algorithms for Scientific Applications
- Theory of Games, Knowledge and Uncertainty
- Advanced Topics in Computational Intelligence
- Advanced Topics in Computational Modes of Learning
Elective courses (at least 6 credits)
- Advanced Topics in Transportation Engineering: Data Analysis
- Deep Learning: Theory and Practice
- Data-driven Security and Privacy
- Physical Systems Applications
- Algorithms for Large Data Sets: Theory and Practice
- Applied Modern Multivariate Statistical Learning
- Statistical Natural Language Processing
- Advanced Business Analytics
- Applied Computational Intelligence
Research and Creative Projects (4 credits)
- Research Colloquia (1 credit)
- Creative Component (3 credits)
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