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This course introduces advanced theory, algorithms, and applications of machine learning. Topics include: advanced ensemble learning methods; statistical learning theory; advanced applications such as recommendation system; nonlinear dimension reduction and data visualization; spectral clustering; generative models such as VAE and GAN; semi-supervised learning; graph neural networks; causal inference, causal discovery, and causal machine learning; privacy and fairness in machine learning; explainability in machine learning. This course requires students to have intermediate mathematical and programming backgrounds in machine learning and deep learning.
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference.
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.
