UC.Berkeley CS189讲义教材:《机器学习全面指南》，185页pdf

【导读】UC.Berkeley CS189 《Introduction to Machine Learning》是面向初学者的机器学习课程在本指南中，我们创建了一个全面的课程指南，以便与学生和公众分享我们的知识，并希望吸引其他大学的学生对伯克利的机器学习课程感兴趣。

课程主页： https://www.eecs189.

讲义目录：

• Note 1: Introduction

• Note 2: Linear Regression

• Note 3: Features, Hyperparameters, Validation

• Note 4: MLE and MAP for Regression (Part I)

• Note 5: Bias-Variance Tradeoff

• Note 6: Multivariate Gaussians

• Note 7: MLE and MAP for Regression (Part II)

• Note 8: Kernels, Kernel Ridge Regression

• Note 9: Total Least Squares

• Note 10: Principal Component Analysis (PCA)

• Note 11: Canonical Correlation Analysis (CCA)

• Note 12: Nonlinear Least Squares, Optimization

• Note 13: Gradient Descent Extensions

• Note 14: Neural Networks

• Note 15: Training Neural Networks

• Note 16: Discriminative vs. Generative Classification, LS-SVM

• Note 17: Logistic Regression

• Note 18: Gaussian Discriminant Analysis

• Note 19: Expectation-Maximization (EM) Algorithm, k-means Clustering

• Note 20: Support Vector Machines (SVM)

• Note 21: Generalization and Stability

• Note 22: Duality

• Note 23: Nearest Neighbor Classification

• Note 24: Sparsity

• Note 25: Decision Trees and Random Forests

• Note 26: Boosting

• Note 27: Convolutional Neural Networks (CNN)

讨论目录：

• Discussion 0: Vector Calculus, Linear Algebra (solution)

• Discussion 1: Optimization, Least Squares, and Convexity (solution)

• Discussion 2: Ridge Regression and Multivariate Gaussians (solution)

• Discussion 3: Multivariate Gaussians and Kernels (solution)

• Discussion 4: Principal Component Analysis (solution)

• Discussion 5: Least Squares and Kernels (solution)

• Discussion 6: Optimization and Reviewing Linear Methods (solution)

• Discussion 7: Backpropagation and Computation Graphs (solution)

• Discussion 8: QDA and Logistic Regression (solution)

• Discussion 9: EM (solution)

• Discussion 10: SVMs and KNN (solution)

• Discussion 11: Decision Trees (solution)

• Discussion 12: LASSO, Sparsity, Feature Selection, Auto-ML (solution)

教材：

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