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Outline Syllabus

Data Mining: motivation and basic ideas

Supervised learning

Classification, Associations, Probabilistic methods

Evaluation Methodology

Unsupervised learning

Clustering

Stream Mining

Advanced methods and Applications

Lectures

1 15/01/15 Introduction  
2 22/01/15 Introduction to supervised learning L02 solutions
3 29/01/15 Association Rules and Decision Trees L03 solutions
4 05/02/15 Neural Networks and Support Vector Machines L04 solutions
5 12/02/15 Regression L05 solutions
6 19/02/15 Bayesian methods  
7 26/02/15 Unsupervised Learning  
8 05/03/15 Exploring Features  
9 12/03/15 Project work  
10 19/03/15 Stream Mining  
11 26/03/15 Project presentations  
12 02/04/15 Revision & sample exam paper