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This is a fundamental course to provide the general concepts of machine learning. This course provides you the opportunity to learn skills and content to practice and engage in scalable machine learning methods on massive data and to study methods to train/learn/develop computational models from varieties of data. It covers supervised learning, unsupervised learning, decision trees, neural networks.
This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
A great introduction to machine learning from a world-class practitioner
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.
It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
