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Frontmatter
Introduction
Tutorial 1 : Python basics
Supervised Learning Problem
Tutorial: Supervised Learning Problem and Least Squares
Ordinary Least Squares
Overfitting/Underfitting and Bias/Variance
Tutorial: Overfitting/Underfitting and Bias/Variance
Regularization, Model Selection and Evaluation
Tutorial: Regularization, Model Selection and Evaluation
Classification I: Generative models
Tutorial on Classification I: Generative models
Classification II: Discriminative models
Tutorial on Classification II: Dimension reduction
Introduction to Unsupervised Learning with a Focus on PCA
Tutorial: Introduction to Unsupervised Learning with a Focus on PCA
Ensemble Methods for Regression
Tutorial: Ensemble Methods for Wind Capacity Factor Prediction
Tutorial: Ensemble Methods for Electricity Demand Prediction
Neural Networks
Tutorial: Universal approximation theorem
Neural network regularization and deep learning
Projects
Resources
Appendix: Matrix calculus
Appendix: A primer on random variables and probabilities
Appendix: Elements of Probability Theory
Appendix: Supplementary Material
Index