A Course in Machine Learning

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3Geometry and Nearest Neighbors 29 4The Perceptron 41 5Practical Issues 55 6Beyond Binary Classification 73 7Linear Models 87 8Bias and Fairness 104 9Probabilistic Modeling 116 10Neural Networks 129. 5 11Kernel Methods 141 12Learning Theory 154 13Ensemble Methods 164 14Efficient Learning 171

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