Courses

Courses offered in the Fall semester (30 ECTS)

• Probability Theory & Introduction to Machine Learning (7ECTS)
• Practical Data Science and Applications (7ECTS)
• Programming and Database Fundamentals (7ECTS)
• Optimization (7ECTS)
• Research seminar/ Independent Study/Capstone project (2ECTS)

Courses offered in the Spring semester (30 ECTS)

• Machine Learning (Required, 7ECTS)
• Research seminar/ Independent Study/Capstone project (Required, 2ECTS)
• At least one from Group A
  • Big Data Processing and Analysis (A, 7ECTS)
  • Time Series Modeling and Analysis (A, 7ECTS)
  • Probabilistic Graphical Models and Inference Algorithms (A, 7ECTS)
  • Detection and Estimation Theory (A, 7ECTS)
• At most two from Group B
  • Advanced Concepts in Machine Learning and Pattern Recognition (B, 7ECTS)
  • Quantum Machine Learning, Optimization and Applications (B, 7ECTS)
  • Quantum Information and Quantum Estimation (B, 7ECTS)
  • Secure Systems (B, 7ECTS)
  • Nonlinear Systems (B, 7ECTS)
  • Reinforcement learning and Dynamic Optimization (B, 7ECTS)
  • Decision Making and Learning in Multiagent Worlds (B, 7ECTS)

More information about the courses can be found on TUC eclass