Support Vector Machines
Lecture Slides
SVM Applet

IMPORTANT ANNOUNCEMENTS!!!

• Project proposal is due Monday, April 9. You can find benchmark datasets for your project from following addresses:
  UCI Machine Learning Repository
  StatLib Datasets Archive
  WebKB Datasets
   Datasets Collected by Weka
  
• Midterm exam will be held on Monday, April 16 between 12:00 and 14:00 in ETAB4.

Instructor: Ethem ALPAYDIN, alpaydin AT boun DOT edu DOT tr
TA: Mehmet GÖNEN, gonen AT boun DOT edu DOT tr

Reference Books

• E ALPAYDIN. Introduction to Machine Learning, The MIT Press, 2004. One copy is on reserve at the Library.
• J HAN, M KAMBER. Data Mining: Concepts and Techniques, Morgan Kauffmann, 2001.
• I WITTEN, E FRANK. Data Mining: Practical Machine Learning Tools and Techniques , 2nd Edition, Morgan Kauffman, 2004.

Topics

1. Introduction
2. Supervised Learning
3. Bayesian Decision Theory
4. Clustering
5. Nonparametric Methods
6. Decision Trees
7. Neural Networks
8. Model Assessment and Comparison
9. Data Preprocessing
10. Data Warehouses and Data Mining

Computer Usage

Homeworks require using Matlab or Weka.

Grading

• Homeworks 0.30
• 1 Project 0.30
• 1 Midterm 0.20
• 1 Final 0.20