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
- Note 4 : MLE and MAP for Regression (Part I)
- Note 5 : Bias-Variance Tradeoff
- Note 6 : Multivariate Gaussians
- Discussion 2 (solution)
- Homework 1 (data) (solution)
- Homework 2 (data) (solution)
- Slides 9/10 (pdf)
- Slides 9/12 (pdf)
Week 3 Overview
Weighted LS, MVN, Kernels
- Note 7 : MLE and MAP for Regression (Part II)
- Note 8 : Kernels, Kernel Ridge Regression
- Discussion 3 (solution)
- Homework 3 (data) (solution)
- Slides 9/17 (pdf)
- Slides 9/19 (pdf)
Week 4 Overview
Weighted LS, Kernels, PCA
- Note 8 : Kernels, Kernel Ridge Regression
- Note 9 : Total Least Squares
- Discussion 4 (solution)
- Homework 3 (data) (solution)
- Homework 4 (data) (solution)
- Slides 9/24 (pdf)
- Slides 9/26 (pdf)
Week 5 Overview
PCA, Total LS, CCA
Week 6 Overview
PCA, Total LS, CCA
- Note 9 : Total Least Squares
- Note 10 : Principal Component Analysis (PCA)
- Note 11 : Canonical Correlation Analysis (CCA)
- Discussion 6 (solution)
- Homework 4 (data) (solution)
- Slides 10/8 (pdf)
- Slides 10/10 (Power Outage)
Week 7 Overview
Midterm Review
- Note 10 : Principal Component Analysis (PCA)
- Note 11 : Canonical Correlation Analysis (CCA)
- Slides 10/15 (pdf)
- Slides 10/17 (Midterm)
Week 8 Overview
Non-linear LS, Gradient Descent, Neural Nets
Week 9 Overview
Classification, Logistic Regression, SVM, NNs, LDA
Week 10 Overview
K-Means, GMMs, EM, ROC Curves
- Note 19 : Expectation-Maximization (EM) Algorithm, k-means Clustering
- Discussion 9
- Homework 6 (data)
- Homework 7 (data)
- Slides 11/5
- Slides 11/7
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)
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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)
- 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
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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) (solution)
- Homework 4: Kernels, Correlations, and Total Least Squares (data) (solution)
- Homework 5: Optimization, Correlation, CCA (data) (solution)
- Homework 6: Backprop and Neural Networks (data)
- Homework 7: K-Means and EM (data)
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Lecture Slides
See Syllabus for more information.
- Slides 8/29 (pdf) (pdf)
- Slides 9/3 (pdf)
- Slides 9/5 (pdf)
- Slides 9/10 (pdf)
- Slides 9/12 (pdf)
- Slides 9/17 (pdf)
- Slides 9/19 (pdf)
- Slides 9/24 (pdf)
- Slides 9/26 (pdf)
- Slides 10/1 (pdf)
- Slides 10/3 (pdf)
- Slides 10/8 (pdf)
- Slides 10/10 (Power Outage)
- Slides 10/15 (pdf)
- Slides 10/17 (Midterm)
- Slides 10/22
- Slides 10/24
- Slides 10/29
- Slides 10/31
- Slides 11/5
- Slides 11/7
- Slides 11/12
- Slides 11/14
- Slides 11/19
- Slides 11/21
- Slides 11/26
- Slides 11/28 (Thanksgiving)
- Slides 12/3
- Slides 12/5
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