CS 189 at UC Berkeley

# Introduction to Machine Learning

Lectures: T/Th 12:30-2 p.m., 155 Dwinelle

## Instructor Stella Yu

stellayu (at) berkeley.edu

Office Hours: Tu/Th 2-3 p.m. 400 Cory (see calendar)

## Professor Anant Sahai

sahai (at) eecs.berkeley.edu

Office Hours: Tu/Th 2-3 p.m. 400 Cory (see calendar)

### Week 1 Overview

## Least Squares Framework

### Week 2 Overview

## Features, Regularization, Hyperparameters and Cross-Validation

### Week 3 Overview

## MLE, MAP, OLS, Bias-Variance Tradeoffs

### Week 4 Overview

## Weighted LS, Total LS, Eigenmethods

- Note 6 : Weighted Least Squares (Draft)
- Note 7 : Total Least Squares (Draft)
- Discussion 04 (solution)
- Discussion 05 (solution)
- Homework 02 (TeX) (data) (solution) (self-grade)
- Homework 03 (TeX) (data) (solution) (self-grade)
- Homework 04 (TeX) (data) (solution) (self-grade)

### Week 5 Overview

## CCA, Feature Discovery, Nonlinear LS, Gradient Descent

- Discussion 05 (solution)
- Homework 03 (TeX) (data) (solution) (self-grade)
- Homework 04 (TeX) (data) (solution) (self-grade)
- Homework 05 (TeX) (data)

## Notes

See Syllabus for more information. You can find a list of week-by-week topics.

- Note 1: Least Squares
- Note 2: Feature Engineering, Ridge Regression
- Note 3: Hyperparameters, Cross-Validation
- Note 4: Gaussians, MLE, MAP
- Note 5: Bias-Variance Tradeoff
- Note 6: Weighted Least Squares (Draft)
- Note 7: Total Least Squares (Draft)

## Discussions

The discussion sections may cover new material and will give you additional practice solving problems. You can attend any discussion section you like. However, if there are fewer desks than students, then students who are officially enrolled in the course will get seating priority. See Syllabus for more information.

- Discussion 01: Review, Least Squares (solution)
- Discussion 02: Ridge Regression
- Discussion 03: Bias-Variance Tradeoff (solution)
- Discussion 04: Multivariate Gaussians (solution)
- Discussion 05: PCA, CCA, and Convexity (solution)

## Homeworks

All homeworks are graded and it is highly-recommended that you do them. Your lowest homework score will be dropped, but this drop should be reserved for emergencies. See Syllabus for more information.

- Homework 0: Course Logistics (solution) (self-grade)
- Homework 01: Review and Least Squares (TeX) (data) (solution) (self-grade)
- Homework 02: Ridge Regression (TeX) (data) (solution) (self-grade)
- Homework 03: Probabilistic Models (TeX) (data) (solution) (self-grade)
- Homework 04: Total Least Squares (TeX) (data) (solution) (self-grade)
- Homework 05: Canonical-Correlation Analysis (TeX) (data)