CmpE 591 Empirical Methods in Artificial Intelligence
Spring 2004
Goals In this course we discuss empirical methods for experiment design and statistical analysis.
Instructor Ethem Alpaydin,
Professor, alpaydin AT boun.edu.tr
Textbook Paul R. Cohen,
Empirical Methods for Artificial Intelligence, The MIT Press,
1995 and extra material (see below)
Discussion leader, topic by week:

Oya: (Cohen, Ch 2) Exploratory Data Analysis

Itir: (Cohen, Ch 3) Basic Issues in Experiment Design

Ersin: (Cohen, Ch 4) Hypothesis Testing and Estimation

Onur: (Cohen, Ch 5) ComputerIntensive Statistical Methods

Baris: (Cohen, Ch 6) Performance Assessment

Kemal: (Cohen, Ch 7) Explaining Performance: Interactions and Dependencies

Cetin: (Cohen, Ch 8) Modeling

Arzucan: (Cohen, Ch 9) Tactics for Generalization
 Levent: (Witten, Frank Data Mining Morgan Kaufmann Ch 5) "Credibility: Evaluating what's been learned"

Ali: Dietterich "Approximate statistical tests for comparing supervised classification learning algorithms" Neural Computation 1998, Alpaydin "Combined 5x2 cv F test for comparing supervised classification learning algorithms" Neural Computation 1999

Alp: Adams, Hand "Improving the practice of classifier performance assessment" Neural Computation 2000, Salzberg, "On comparing classifiers: A critique of current research and methods" Data Mining and Knowledge Discovery

Burak: (Conover Practical Nonparametric Statistics 3rd Ed Wiley Secs 2.5, 3.1,3.2) Some tests based on the binomial distribution

Aydin: (Conover Secs 3.35) Sign test

Bugra: (Conover Secs 4.14) Contingency tables

Utku: (Conover Secs 4.58) Goodness of fit tests
Prerequisite None.
Computer Usage Students
will individually do homeworks and complete a project.
Total Credits 3
Grading

Weekly Quizzes 0.5

Project 0.2

Homeworks 0.3