CS189

CS 189 at UC Berkeley

Introduction to Machine Learning

Lectures: 2:30 - 4pm Mon-Thurs in LeConte 4

Instructor Ke Li

ke.li [at] eecs.berkeley.edu

Office Hours: TBA

Instructor Josh Tobin

jtobin [at] berkeley.edu

Office Hours: TBA

Week 0 Overview

Linear Regression, Features, Hyperparameters and Cross-Validation

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
  • Discussion 01: Derivatives Review, Least Squares
  • Discussion 02: Ridge Regression
  • Discussion 03: Bias-Variance Tradeoff
  • Discussion 04: Kernel and Multivariate Gaussians
  • Discussion 05: Dimensionality reduction
  • Discussion 06: Midterm Review
  • Discussion 07: Backpropagation
  • Discussion 08: GD/SGD
  • Discussion 09: QDA and Logistic Regression
  • Discussion 10: Expectation Maximization
  • Discussion 11: SVMs/Nearest Neighbors
  • Discussion 12: Orthogonal Matching Pursuit
  • Discussion 13: Convolutional Neural Networks
  • Discussion 14: Clustering
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Homeworks

All homeworks are fully graded. Your lowest homework score will be dropped, but this drop should be reserved for emergencies. See Syllabus for more information.

  • Homework 0: Review and Linear Regression
  • Homework 01: Least Squares
  • Homework 02: Ridge Regression
  • Homework 03: Probabilistic Models
  • Homework 04: Kernel methods
  • Homework 05: Dimensionality reduction
  • Homework 06: CCA and Midterm Redo
  • Homework 07: Backpropagation
  • Homework 08: SGD and Classification
  • Homework 09: LDA, CCA
  • Homework 10: K Means and EM
  • Homework 11: SVMs and Neighbors
  • Homework 12: Sparsity and Decision Trees
  • Homework 13: Boosting, Convolutional Neural Networks
  • Homework 14: K-SVD and Dropout
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