S1

Machine Learning 2 is an advanced course that extends the fundamental concepts of machine learning by focusing on ensemble methods, probabilistic models, and feature engineering techniques. The course emphasizes both theoretical understanding and practical implementation using Python and real-world datasets, including structured and audio data.

Students will explore powerful learning paradigms such as decision trees, ensemble methods (bagging, random forests, boosting), probabilistic clustering models (GMM with EM), and advanced feature representation techniques, particularly for audio signals.