Reading and Lecture Plan

This page contains the reading assignments and course pace for CMPS 242, Fall 2018.

It will contain tentative information that is intended to be updated regularly.  Unless indicated otherwise the readings are from Bishop's book.

Mitchell's book Machine Learning seems to be available on the web, and a copy is on reserve in the science library.

 

Dates Reading Topics
9/27 1.0, 1.1, 1.3,1.51, 1.52 Introduction
10/2, 10/4 1.2, 1.5 Probability and Bayesian learning
10/4 2.3-2.34, Gaussian distributions
 10/9 Mitchell 6.9,6.10 Naive Bayes and text
 10/11  1.4, 2.5,  Instance based learning
 10/18  3 through 3.3.2  Linear regression
 10/13 Ch 4 through 4.3 Linear classification, Fisher's linear discriminant, perceptron algorithm, logistic regression
 10/30  14.4 Decision Trees
 11/2

 Ch 5 through 5.3, 5.5.1,  5.5.2, 5.5.6

backprop handout

Neural Networks

 

 

(see attachment below)
 11/13 Ch 6 through eq. (6.27)  Kernels and Kernel trick
 11/13 Section 7.1 Support vector machines
 11/20 Chapter 9 through 9.3.3 K-means and EM
  Chapter 13 through 13.2.1 Hidden Markov models (details not emphasized)
  12.1 Principle Components Analysis