Spring 2006: Introduction to Machine Learning (CMSC 726)

Lectures (TENTATIVE)

Date Topic Reading(s) Slides Handouts
Jan 26 Introduction Mitchell, ch. 1 Introduction Homework 1
Jan 31 Concept Learning 101 Mitchell, ch. 2 Concept Learning
Feb 2 Parameter Estimation 101 Notes handed out 1/30 Statistics 101
Feb 7 Linear Models
  • Required: Mitchell's Chapter on Naive Bayes and Logistic Regression (link)
  • Optional: Ng and Jordan's NIPS 2001 paper on Discriminative versus Generative Learning (link)
Linear Classifiers (updated 2/14) Homework 2
Feb 9 NO CLASS
Feb 14 Linear Models
Feb 16 Evaluating Hypothesis Mitchell, ch. 5 Evaluation
Feb 21 Non-Linear Models Mitchell, ch. 3 Decision Trees Project
Feb 23 Non-Linear Models Mitchell, ch. 4 Neural Netorks
Feb 28 Non-Linear Models finish ch. 4, start ch. 8 Homework 3
Mar 2 Non-Linear Models Mitchell, ch. 8 Instance-based Learning
Mar 7 Max Margin Approaches SVMs
Mar 9 Max Margin Approaches
Mar 14 Computational Learning Theory Mitchell, ch. 7 Learning Theory
Mar 16 More Bias/Variance, Ensemble Methods Material handed out in class Ensemble Methods Administrivia Slides Homework 4
Mar 21 Spring Break
Mar 23 Spring Break
Mar 28 Unsupervised Learning Clustering Tutorial Evaluation, part II Clustering Slides, part I
Mar 30 MIDTERM
Apr 4 Unsupervised Learning Clustering Slides, part II
Apr 6 Spectral Clustering Spectral Clustering Slides
Apr 11 Graphical Models Material handed out in Class Graphical Models, part I
Apr 13 Graphical Models Material handed out in Class Graphical Models, part II Homework 5
Apr 18 Graphical Models
Apr 20 Graphical Models Graphical Models, part III
Apr 25 Graphical Models
Apr 27 Reinforcement Learning Sutton & Barto Book - Reinforcement Learning: An Introduction Reinforcement Learning
May 2 Project Presentations
May 4 Project Presentations
May 9 Project Presentations
May 11 Review