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.

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