Machine Learning
Syllabus (Reading in Witten)
week 1
Chapter 1: intro and examples,
Lecture 1 Lecture 2 Lab 1 Hw 1
week 2
Chapter 2: Concepts and instances
Chapter 10 : Weka
Chapter 11 : Weka Explorer skim pp 407 - 417, for Lab: 432 - 443
Lecture 3 Lecture 4 Lab 2
week 3
Chapter 3: Knowledge Representation
Chapter 11 : Weka Explorer pp 417-432
lecture 5 lecture 6
HW1 solutions Lab 3
week 4
Chapter 4: Algorithms
Chapter 11: Weka Explorer pp 432 - 451
Hw 2 Lab 4
Lecture 7
week 5
Midterm
Chapter 4: Linear Models
Chapter 5: Evaluation of Learning: cross validation
Chapter 11: Weka Explorer pp 432 - 451
Lecture 8 Lecture 9
week 6
Chapter 5: Evaluation of learning: recall, percision
Chapter 12: Weka Knowledge Flow
lab: using a t-test to compare algorithms
Hw 3 Lab 5
Lecture 10 Lecture 11
week 7
Chapter 6: Decision Trees, Mixture Models, Neural Networks pp 191-241
Lab: k-means clustering. Hw 4 Lab 6
Lecture 12 Lecture 13
week 8
Chapter 6: support vector machines, radial basis functions, instance-based clustering
Hw 5 Solutions: Hw 5 Lecture 14 Lecture 15
special lab: induction and loop invariants Lab 7
week 9
Review and Final
week 10
Project presentations
Chapter 6: Expectation Maximization (EM)
