This course covers the foundations of statistical machine learning, with an emphasis on probabilistic and statistical methods for prediction and clustering in high dimensions. Topics include regression, support vector machines, dimensionality reduction, expectation-maximization, hidden Markov models, and deep learning.
This graduate-level course focuses on continuous and discrete linear systems, including state-space representations, stability, controllability, observability, realization, and feedback stabilization.
This course develops fundamental programming and problem-solving skills through the implementation of algorithms and data structures in C and C++. Students gain hands-on experience with programming tools and software development through practical laboratory exercises.