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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