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Lecture Notes on Machine Learning - Kevin Zhou

Lecture Notes onMachine LearningKevin Notes follow Stanford s CS 229 Machine Learning course, as offered in Summer 2020. Othergood resources for this material include: Hastie, Tibshirani, and Friedman,The Elements of Statistical Learning . Bishop,Pattern Recognition and Machine Learning . Wasserman,All of Statistics. Russell and Norvig,Artificial Intelligence: A Modern Approach. Mackay,Information Theory, Inference, and Learning Algorithms. Michael Nielsen s online book,Neural Networks and Deep Learning . Jared Kaplans s Contemporary Machine Learning for Physicists Lecture Notes . A High-Bias, Low-Variance Introduction to Machine Learning for most recent version is here; please report any errors found to Supervised Introduction.

• Broadly speaking, ML can be broken into three categories: supervised learning, unsupervised learning, and reinforcement learning. • Supervised learning problems are characterized by having a \training set" that has \correct" labels. Simple examples include regression, i.e. tting a curve to points, and classi cation.

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