CS189

CS 189 at UC Berkeley

Introduction to Machine Learning

Lectures: T/Th 3:30-5 p.m., 155 Dwinelle

Professor Jennifer Listgarten

jennl [at] berkeley.edu

Office Hours: Tu/Th 5-6 p.m. (see calendar)

Professor Anant Sahai

sahai (at) eecs.berkeley.edu

Office Hours: Tu/Th 5-6 p.m. (see calendar)

Week 0 Overview

Least Squares Framework

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 01: Review, Least Squares
  • Discussion 02: Ridge Regression
  • Discussion 03: Bias-Variance Tradeoff
  • Discussion 04: Multivariate Gaussians
  • Discussion 05: PCA, CCA, and Convexity
  • Discussion 06: Gradient Descent
  • Discussion 07: Backpropagation
  • Discussion 09: LDA/QDA/SGD
  • Discussion 10: SGD/SVM
  • Discussion 11: Kernels/Nearest Neighbors
  • Discussion 13: Convolutional Neural Networks
  • Discussion 14: Clustering
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Homeworks

All homeworks are partially 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. Here is the semester's self-grade form (See form for instructions). See Syllabus for more information.

  • Homework 0: Course Logistics
  • Homework 01: Review and Least Squares
  • Homework 02: Ridge Regression
  • Homework 03: Probabilistic Models
  • Homework 04: Total Least Squares
  • Homework 05: Canonical-Correlation Analysis
  • Homework 06: Gradient Descent
  • Homework 07: Backpropagation
  • Homework 08: Midterm Redo
  • Homework 09: Classification and SGD
  • Homework 10: Support Vector Machines
  • Homework 11: Kernels and Neighbors
  • Homework 12: Sparsity and Decision Trees
  • Homework 13: (Convolutional) Neural Networks
  • Homework 14: K-SVD
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