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Course schedule - Fall semester 2023-2024
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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 (7ECTS)
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)
Online learning and Dynamic Optimization (B, 7ECTS)
Decision Making and Learning in Multiagent Worlds
(B, 7ECTS)
More information about the courses can be found on
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