School of Information Technologies

28/2/2011

Watch out for news here

28/2/2011

Welcome to COMP5318!

28/4/2011

The marks for assignment 1 can be found here.

30/5/2011

Assignment 2 will be available on Wednesday June 1st for collection at the reception.

20/6/2011

The marks for assignment 2 can be found here.

Activity

Day

Time

Venue

Lectures

Monday

6-8pm

Carslaw lecture theatre 175

Laboratory/Tutorial
(start in Week 2)

Monday

8-9pm

Carslaw labs 202A, 202B and 202C

Assignement

%

Marks

Due

Individual/Group

Notes

Late submission policy

Ass1: Test

15

Marks

w6, in class

Individual

In the 1st first hour of the lectures (6-7pm). Semi-open as the exam. Students are allowed  1 sheet of their own notes (A4-size, double-sided, handwritten or typed). The test will cover the material till week 5.

Not possible to re-sit the test.

Ass2: Data Analysis

20

w9, Friday 5pm

Individual or in pairs (groups of more than 2 people are not allowed)

Submission: 1) hard copy in the locker labelled COMP5318 located in the School of IT Building, level 1, in the postgraduate labs wing and  2) electronically via webCT

- A penalty of 1 mark per each day after the deadline will apply
- Assignments will not be accepted if the delay is more than 7 days

Ass3:
Research paper presentation

 final schedule

15

 w12 and 13, in class

Group

- No late presentations are allowed; a student who is unable to present on the specified date will receive 0 marks for this assessment

Written exam

50

examination period

Individual

The exam will be semi-open. You are allowed 1  sheet of  your own notes (hand-written or typed, double-sided, A4-size) and a non-programable calculator (you don't need a calculator). No other material is allowed (no book, no additional notes). The exam will be on all material covered in the review slidesexcept Clustering.

Week

Date

Topic

1

28th Feb

Admin matters. Assumed Knowledge Check. Introduction to Data Mining (DM); challenges, origins, DM vs Machine Learning and Knowledge Discovery in Databases; DM tasks.

Data: types, cleaning (noise, missing values), pre-processing (aggregation, feature selection, discretization and binarization, normalization), similarity measures.

2

7 March

Data
An overview of datatypes and data transformations
Tutorial

3

14 March

Association rules 1: Introduction, Mining frequent items, Apriori algorithm
Tutorial

4

21 March

Association Rules Continued and Basics of Probability
Tutorial .

5

28 March

Clustering: Kmeans and hierarchical clustering
Clustering; Tutorial

6

4 April

Ass1: Test
Soft K-means and Expectation Maximisation

EM
Tutorial

7

11 April

EM and Dimensionality Reduction:

Lecture Tutorial

8

18 April

Classification 1:
k-nearest neighbours, naïve Bayes, evaluating a classifier.
Lecture Tutorial
Assignment 2 available here.

-

25 April

Easter Holiday

9

2 May

Classification 2:
Discriminative classifiers: Logistic regression, Support vector machines (SVM); maximum margin hyperplane, finding a maximum margin hyperplane as an optimisation problem, linear SVM with hard and soft margin, nonlinear SVM (kernel trick and Mercer’s theorem).
Lecture Tutorial
Ass2: Data analysis due Friday, 6 of May, 5pm

10

9 May

Outlier detection
Lecture   Tutorial

11

16 May

12

23 May

Ass3: Student presentations of research papers.

13

30 May

Ass3: Student presentations of research papers.
Review of course material
The report is due for submission on Friday June 3rd by 5pm from the Blackboard website

Introduction to Data Mining
Pang-Ning Tan, Michael Steinbach, Vipin Kumar,
Pearson Education (Addison Wesley), 0-321-32136-7, 2006

Chapters 4, 6 and 8 are freely available here and from the publisher.

Data mining - practical machine learning tools and techniques with Java implementations, 2d edition
Ian H. Witten, Eibe Frank
Morgan Kaufmann. 0-12-088407-0, 2005

Machine Learning view of Data Mining. Very readable. The book of the WEKA library.

Data Mining: Introductory and Advanced Topics
Margaret Dunham, Prentice Hall, 0-13088892-3, 2003

Good coverage of the topics included in the course. Very readable. Pseudo code and computation complexity covered.

Data Mining Concepts and Techniques. 
 J. Han and M. Kamber
Morgan Kaufmann, 2006, ISBN 1-55860-901-6 

Database view of Data Mining.

Principles of Data Mining
D. Hand, H. Mannila, P. Smyth, Principles of data mining,
MIT Press, 2001, ISBN: 0-262-08290-X

Statistical view of Data Mining. Advanced, requires good statistical knowledge.