CS 8751: Advanced Machine Learning
Fall 2005
Take Home Final
Lecturer:
Rich Maclin
(rmaclin)
Syllabus
Grades
Class References:
Mitchell Textbook Web Page
Witten and Frank Data Mining Textbook Page (for Weka code)
UCI Repository of ML Data Sets
Class Materials:
Introduction (Mitchell Chapter 1, Cristiannini and Shawe-Taylor 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 (M6)
Genetic Algorithms (Evolutionary Computing) (M7)
Rule Learning (M8)
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)
PAC Theory
(M7)
Other classification methods
Kernel Methods
(Cristianini & Shawe-Taylor, Chs 1-6)
Hybrid Methods
(M11)
Ensemble Learning
Read Opitz & Maclin paper
Mining Association Rules
Read the Apriori Paper
More Bayes networks notes (from Stuart Russell for Russell & Norvig's AI):
Bayes Nets (part 1)
Bayes Nets (part 2)
Program Assignments:
Program 1
Program 2
Program 3
Homework Assignments:
Homework 1
Homework 2
Homework 3
Homework 4
Project Ideas
Sample Exam Questions:
Midterm Exam Sample Questions
Some Useful Links:
UCI Repository of ML Data Sets
OFAI bibliography server (AI reference lookup)
A somewhat dated set of
Links to ML and CBR people
Oral Presentation Advice