CSED490R Introduction to Machine Learning, Spring 2018


Announcements

Primary Textbook

References

Lectures

Dates and Titles Topics Lecture Slides Suggested Further Readings
Lecture 1
Introduction and mathematical preliminaries
  • Introduction
  • Probability
  • Linear algebra
  • Information theory
  • Density estimation


Lecture 2
Regression
  • What is a regression analysis?
  • Linear models for regression
  • Regularization and shrinkage (Ridge and LASSO)
  • Logistic regression


  • Murphy: Chapter 7, 13.1-13.4, 8
  • Bishop: Chapter 3, 4.3
Lecture 3
Nonparametric Regression
  • Nadaraya-Watson kernel regression
  • Gaussian process regression


  • Murphy: Chapter 15.1-15.2
  • Bishop: Chapter 6.3-6.4
Lecture 4
Classification
  • Bayesian decision theory
  • Linear discriminant analysis
  • Naive Bayes classifiers
  • Multilayer perceptrons


  • Murphy: Chapter 3, 4, 28.3
  • Bishop:
Lecture 5
Clustering
  • k-means clustering
  • Mixture of Gaussians


  • Murphy:
  • Bishop:
Lecture 6
Latent variable models
  • EM optimization
  • PCA
  • ICA
  • NMF


  • Murphy:
  • Bishop:

Homework Assignments

  • Hwk 1

  • Hwk 2

  • Hwk 3

  • Hwk 4

  • Hwk 5