CS 5751: Machine Learning

https://www.d.umn.edu/~rmaclin/cs5751/index.html

Spring 2001

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Lecturer: Rich Maclin (rmaclin)

Teaching Assistant: Hariprasad Bommaganti (hbommaga)

Syllabus

Grades

Class Materials:

• Textbook Web Page
• UCI Repository of ML Data Sets
• Chapter 1 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 2 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 3 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 4 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 5 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 8 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 9 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 10 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Clustering Review Read sections 1-5.7, 5.11-5.12 Clustering Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 13 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 11 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 12 Notes (PDF, 6 per page)   (PDF, 1 per page)
• Ensemble Paper   Ensemble Notes (PDF, 6 per page)   (PDF, 1 per page)
• Chapter 6 Notes (PDF, 6 per page)   (PDF, 1 per page)

Program Assignments:

1. Program 1
2. Program 2
3. Program 3 Revised files: train.C   alt3.2.data
4. Program 4
5. Program 5
6. Program 6

Team Assignments

Homework Assignments:

• Homework 1
• Homework 2
• Homework 3
• Homework 4
• Homework 5

Sample Exam Questions:

• Sample questions for Midterm 1
• Sample questions for Midterm 2

Take Home Final

C++ Notes (from 1622/1521) based on Learning C++: A Hands-On Approach by Nagler

• Intro to C++
• C++ Input and Output
• C++ Other Features
• C++ Classes

CS 8751: Advanced Machine Learning

Spring 2009

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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)
  • Another Aside: Hybrid Methods (M12)
  • 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)
  • Ensemble Learning   Read Opitz & Maclin paper
  • Mining Association Rules   Read the Apriori Paper

Program Assignments:

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

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

Project Presentations

Links for sites to find papers:

• Journal of Machine Learning Research (JMLR)
• Machine Learning
• Data Mining and Knowledge Discovery
• International Conference on Machine Learning 2008   2007   2006   2005 (titles only)   2004
• Information about other conferences can be found at:
  • AAAI
  • KDD 2008   2007   2006   2005   2004
  • COLT 2008   2007   2006   2005   2004
• KDnuggets
• ACM SIG-KDD

Some Useful Links:

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