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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 .

3 1. Supervised Learning 1 Supervised Learning 1.1 Introduction We begin with an overview of the sub elds of machine learning (ML). • According to Arthur Samuel, ML is the eld of study that gives computers the ability to learn without being explicitly programmed. This gives ML systems the potential to outperform the programmers that made them.

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