Mathematics for Machine Learning - CO496
The aim of the course is to provide the students the necessary mathematical background and skills in order to understand, design and implement modern statistical machine learning methodologies, as well as inference mechanisms. The course will provide examples regarding the use of mathematical tools for the design of basic machine learning and inference methodologies, such as Principal Component Analysis (PCA), Bayesian Linear Regression and Support Vector Machines.
Advanced Statistical Machine Learning and Pattern Recognition - CO495
The aim of the course is to provide the students the necessary theoretical and computational skills to understand, design and implement modern statistical machine learning methodologies regarding statistical component analysis, statistical linear dynamical systems (i.e., HMMs and Kalman filters) and other statistical models such as Markov Random Fields (MRFs).