This course provides an in-depth examination of the advanced subject of machine learning, designed to deepen the students’ understanding of cutting-edge algorithms and their theoretical foundations. The course explores a range of advanced algorithms, starting with their mathematical principles and progressing into real-world applications. The covered spectrum of advanced techniques ranges from classical approaches such as kernel machines, low-rank and sparse models, and graphical models, to the latest advancements like diffusion models, transformers, and graph neural networks.

CILO-1: Apply mathematical tools such as matrix analysis, Bayesian inference, and spectral theory to analyse and design advanced machine learning algorithms. CILO-2: Utilize advanced machine learning methods to model and interpret complicated real-world data. CILO-3: Identify and analyse complex problems in advanced machine learning, devise appropriate strategies to solve them, and evaluate the outcomes based on established criteria.