The following schedule isteantive and may changeas the course progresses.

Most required readings are taken from the course textbook Machine Learning: A Probabilistic Perspective (MLaPP). We use ESLII to refer to the book Elements of Statistical Learning by Hastie, Tibshirani and Friedman, which is freely available.

There are two types of slides:

  • Lecture slides: URLs in the Lectures column point to slides used in the lectures, which contain administravia, reviewed and new topics, and other course-specific information. Material in those slides are required. Slides in this column are often released prior to the corresponding lectures and are updated (once) after the lectures to make adjustment, correct typos etc.
  • Topic slides: URLs in the Topics column, however, are in general free of course-specific details and they may go into more advanced material that relate to the topics. Not all material in those slides are required --- they are suppmenetary to those who want to know a bit more. As such, slides in this column are work-in-progress and are updated whenver I have time.


Date Topics Lectures Readings Notes
8/26 Overview
Nearest neighbor classification
slides MLaPP: sec. 1.1, 1.2 1.4.2 Take-home special quiz
8/28 Nearest neighbor classification slides MLaPP: sec. 1.1, 1.2, 1.4.2, 2.1 -- 2.5
Recitation slides on basic math
Recitation: special quiz
9/2 No class Labor day ☺
9/4 Linear regression slides MLaPP: sec. 1.2, 1.4.1 -- 1.4.5, 4.1
ESLII: sec. 2.3, 3.1, 3.2 -- 3.2.1
Recitation: basic math
Homework 1 is out
9/9 Linear regression
Logistic regression
slides MLaPP: sec 7.1-7.3, 7.5.1-7.5.2
9/11 Model selection
Logistic regression
slides MLaPP: 1.4.6 - 1.4.8, 8.1-8.3.4 Recitation: Convex duality
9/16 Logistic regression
Gaussian discriminatn analysis
Generative vs. Discriminative
slides MLaPP: 4.2.1 - 4.2.4, 8.6.1 Recitation:
9/18 Generative vs. discriminative
Multinomial logistic regression
slides MLaPP: 8.3.7 Recitation:
Homework 2 is out
9/23 Nonlinear basis functions
Overfitting
slides MLaPP: 6.4.4, 6.5, 7.5 Recitation:
9/25 Bias/variance tradeoff slides ESLII: sec7.1-7.3
Prof. Kakade's note
Recitation:
9/30 Basic concepts in Bayesian approach slides MLaPP: sec 2.4.5, 3.3, 7.6 Mock Quiz 1
10/2 Bayesian linear regression slides MLaPP: sec 2.4.5, 3.3, 7.6
10/7 Kernel methods slides MLaPP: sec 2.4.5, 3.3, 7.6 Homework 3
10/9 Kernel methods
Gaussian Process
slides MLaPP: sec 14.1, 14.2 up to 14.2.5, 14.4, 15.1 -- 15.2.4
10/16 Perceptron
SVM
slides MLaPP: sec 8.5.4 14.5
10/21 SVM slides MLaPP: sec 8.5.4 14.5
10/23 Boosting slides MLaPP: sec 16.4 (up to 16.4.5), 16.4.8 Homework 4
10/28 Clustering, GMM slides MLaPP: sec 1.3 11.2--11.2.1, 11.4--11.4.2, 25.1
11/4 EM
Dimensionality reduction
slides
slides slides
MLaPP: 11.4.3, 12.2.1-12.2.2, 14.4.4
11/11 Bayes nets slides MLaPP: 10.1, 10.2.1 - 10.2.3
11/13, 11/20 Bayes nets slides MLaPP: 10.1, 10.2.1 - 10.2.3, 10.3, 17.1-17.3
Bishop book chapters
Homework 5
11/25 Markov networks slides Bishop book chapters Homework 6


Fei Sha 2014