CS 8751: Advanced Machine Learning

Spring 2008

Lecturer: Rich Maclin (rmaclin)

Syllabus

Grades

Class References:

• Mitchell Textbook Web Page
• WEKA Web site
• Witten and Frank Data Mining Textbook Page
• UCI Repository of ML Data Sets

Class Materials:

• Introduction (Mitchell Chapter 1)
• Classification
  • Classification Basics and Version Spaces (M2)
• Aside: ML Related Statistics and Terminology (M5)
• Classification (continued)
  • Decision Trees (M3)
  • Neural Networks (M4)
  • Instance Based Learning (M8)
  • Genetic Algorithms (Evolutionary Computing) (M9)
  • Rule Learning (M10)
• Unsupervised Learning: Clustering   Clustering Review Paper Read sections 1-5.7, 5.11-5.12
• Agent Learning: Reinforcement Learning (M13)
• Learning Theory
  • Bayesian Methods (M6)
  • Bayesian Networks (part 1) (additional from Russell&Norvig)
  • Bayesian Networks (part 2) (additional from Russell&Norvig)
  • PAC Theory (M7)
• Other classification methods
  • Kernel Methods   (Read Burges' tutorial)
  • Hybrid Methods (M11)
  • Ensemble Learning   Read Opitz & Maclin paper
  • Mining Association Rules   Read the Apriori Paper

Program Assignments:

1. Program 1
2. Program 2
3. Program 3
4. Program 4

Homework Assignments:

1. Homework 1
2. Homework 2
3. Homework 3
4. Homework 4
5. Homework 5

Sample Exam Questions:

• Midterm 1
• Midterm 2
• Final

Presentation Schedule

Links for sites to find papers:

• Journal of Machine Learning Research (JMLR)
• Machine Learning
• Data Mining and Knowledge Discovery
• International Conference on Machine Learning 2007   2006   2005 (titles only)   2004
• Information about other conferences can be found at:
  • AAAI
  • KDD 2007   2006   2005   2004
  • COLT 2007   2006   2005   2004
• KDnuggets
• ACM SIG-KDD

Some Useful Links:

• AI Bibliographies
• The AAAI Machine Learning Web Site
• Oral Presentation Advice