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Students will get familiar with advanced machine learning methods (MLM) that go beyond common data domains (vision, text) taught in the other courses (e.g., BE4M33MPV, BECM36NLPT). They will learn techniques that work well for tabular and structured data (e.g., relational databases), including rule/tree ensembles, graph neural networks, and other advanced approaches aimed at complex learning problems.
Additionally, the course will also teach students methods for model interpretability, the basics of causality, and reinforcement learning.
This is an advanced ML course that assumes at least some prior knowledge of ML (e.g., B4B33RPZ, BECM33MLF), data representation (e.g., B0B36DBS, B0B01LGR), and deep learning (e.g., BECM33DPL).