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A Course in Machine Learning
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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