Knowledge Discovery in Databases (COMP5318)


Semester 2, 2009

Lecturers Class Time Tutorials Office Hours
Sanjay Chawla Monday 6-8 PM Monday 8-9 PM Monday 4-5 PM

Outline:

This unit will cover the basics of data mining theory and applications. We will begin with basic regression techniques, move on to the use of Bayes theorem and cover classical data mining topics like: classification; clustering and outlier detection.

Textbook

The nominal textbook is Introduction to Data Mining by P. Tan, M. Steinbach and V. Kumar

Lectures

Week Topic Lecture Tutorial
1 Introduction Introduction No Tutorial in Week 1
2 Data Exploration and Types Data Types Tutorial 1
3 Visualization and Summary Statistics Visualization Tutorial 2 (Matlab Solution)
4 Association Analysis (Basics Concepts) Association 1 Tutorial 3
5 Random Variables and Distribution Random Variables and Distribution Tutorial 4
5 Classification Classification Tutorial 5
6 Probability and Bayes Theorem Probability and Bayes Theorem;Alternate Classification Tutorial 6
7 Clustering Cluster Analysis Tutorial 7 (Sample code 1, 2)
8 EM Algorithm Expectation Maximization  
11 Covariance Matrix Applications Covariance Matrix Tutorial 8

Assignments Due Date Mark
Assignment1 Sept 21st Mark for assignment1
Assignment2 Oct 26th
Exam Preparation --

Administrative Information

Download the information handout here: download