The course is the prerequisite for the more advanced courses in ML option at IEU Departments of Computer Engineering and Software Engineering. The course's goal is to build mathematical and conceptual foundation for students advancing further in that option. The course covers key background topics from machine learning including signals and sampling, digital filters and Fourier transform, numerical optimization, and the fundamentals of the theory of statistical learning comprising the theoretical foundation of machine learning methods.
Course program
Digital Signal Processing - Signals
Digital Signal Processing - Digital Filters
Digital Signal Processing - Fourier Transform
Optimization - Matrices and vectors
Optimization - Numerical optimization
Optimization - Numerical optimization at very large scales
Statistical Learning - Probability review
Statistical Learning - General concepts
Statistical Learning - Statistical decision theory and function estimation
Statistical Learning - Model assessment and selection
Statistical Learning - Cross-validation and bootstrapping