This course focuses on advanced machine learning topics with an emphasis on the theoretical foundations and the advanced implementation tools/practices. The topics go deep into specific supervised, unsupervised and semi-supervised algorithms addressing state-of-the-art machine learning techniques.

Students will be able to interpret and interrelate different advanced ML algorithms and approaches. Students will know how to approach the data, identify and select the most convenient ML approaches, regularization techniques, monitor training and adjust regularization parameters. Students will master the use of Python based toolboxes.

Neural Networks: Introduction, architectures and training procedures, evaluation and application. Ensemble learning: Bagging and boosting. Clustering - advanced algorithms, mixture models and expectation-maximization (EM) algorithm, ensemble clustering. Semi-supervised algorithms. Hidden Markov models. Probabilistic graphical models (inference, belief propagation, practical application).

Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, workshops.