CS 189 at UC Berkeley
Introduction to Machine Learning
Lectures: 5-6:30 pm Tu-Th in Pimentel 1 (Berkeley Academic Guide page)
Week 0 Overview
Linear Regression, Features, Hyperparameters and Cross-Validation
Week 1 Overview
Features, Linear Regression, Regularization, Hyperparameters and Cross-Validation
- Note 2 : Linear Regression
- Note 3 : Features, Hyperparameters, Validation
- Discussion 1 (solution)
- Homework 0 (solution)
- Homework 1 (data) (solution)
- Homework 2 (data) (solution)
- Slides 9/3 (pdf)
- Slides 9/5 (pdf)
Week 2 Overview
MLE, MAP, Bias-Variance Tradeoff
Week 3 Overview
Weighted LS, MVN, Kernels
Notes
See Syllabus for more information. You can find a list of week-by-week topics. Notes are not a substitute for going to lecture, as additional material may be covered in lecture. Notes are from a previous iteration of the course and may not be comprehensive. Refer to lectures.
- 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)
Discussions
The discussion sections may cover new material and will give you additional practice solving problems. You can attend any discussion section you like. See Syllabus for more information.
- 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)
Homeworks
All homeworks are fully graded. Your lowest homework score will be dropped and homework scores are rescaled such that 80% is full credit, but this drop should be reserved for emergencies. See Syllabus for more information.
- Homework 0: Getting Started (solution)
- Homework 1: Prerequisites (data) (solution)
- Homework 2: Regression (data) (solution)
- Homework 3: Probabilistic Regression (data)