CS 189 Spring 2014

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Syllabus: [pdf]

Schedule


Week Topics Slides Reading Discussion Homework
1-2 Introduction, MLE [pdf] Murphy 2.1-2.5 [pdf]
3 Decision theory, linear algebra review, Gaussians [pdf] [pdf] Murphy 4.1-4.2 (starred sections optional)
Multivariate Gaussians
LDA
[pdf] HW2 solutions [pdf]
4 Linear regression [pdf] Murphy 7.1-7.3, 7.5.1 [pdf]
5 Logistic regression, optimization, support vector machines [pdf] Murphy 8.1-8.3.3 [pdf]
6-7 SVMs continued, nonparametric methods [pptx] Murphy 14.5 (excluding 14.5.1)
Burges [pdf]
Loss Functions [pdf]
[pdf]
8 Midterm [pdf]
9 Nearest neighbor [pdf] Benefits of Non-Parametric Methods [pdf] (only read introductory part) [pdf]
10 Decision trees [ppt] Adaboost [pdf] [pdf]
11 Neural networks [ppt] Backpropagation [pdf]
Lectures from Professor Yaser Abu-Mostafa (Caltech)
[pdf]
12 Unsupervised learning, clustering [pdf] [pdf]
13 Mode seeking [ppt] [pdf]
14 Dimensionality reduction, PCA [ppt] SVD [pdf] [pdf]

Staff

Instructors

Prof. Jitendra Malik
malik@eecs.berkeley.edu
Office Hours: Monday 11-12, 722 Sutardja Dai

Prof. Alyosha Efros
efros@eecs.berkeley.edu
Office Hours: Tuesday 3:40-5, 724 Sutardja Dai

TAs

Jonathan Ho
jonathanho@berkeley.edu
Office Hours: Thursday 11-12pm, 651 Soda

Sharad Vikram
sharad.vikram@gmail.com
Office Hours: Friday 12-1pm, 611 Soda

Christopher Xie
chrisdxie@berkeley.edu
Office Hours: Tuesday 4-5pm, 411 Soda

Ning Zhang
nzhang@eecs.berkeley.edu
Office Hours: Monday 10-11am, 611 Soda