This course presents advanced materials on datamining, pattern recognition, and machine learning

A brief outline of the course:

Machine learning techniques are widely used in many computing applications; for example, in web search engines, spam filtering, speech and image recognition, computer games, machine vision, credit card fraud detection, stock market analysis and product marketing applications. Machine learning implies that there is some improvement that results from the learning program having seen some data. The improvement can be in terms of some performance program (e.g., learning an expert system or improving the performance of a planning or scheduling program), in terms of finding an unknown relation in the data (e.g., data mining, pattern analysis), or in terms of customizing adaptive systems (e.g., adaptive user-interfaces or adaptive agents).

In the first half of the paper, we will provide an overview of the learning problem and the view of learning as search. We will study several techniques for learning such as Rule Learning, Exhaustive Learning, Genetic Algorithms, Reinforcement Learning, Neural Networks, and Inductive Logic Programming. In addition we will provide an overview of the experimental methods necessary for understanding machine learning research.

The second half of the paper will be on Advanced Pattern Recognition. This will embrace recognition and learning techniques based on probabilistic data models including basics of empirical risk minimisation, graphical data models (Bayesian networks and Markov random fields), exact and approximate Bayesian decisions for these models, e.g. iterative graph cuts and loopy belief propagation, as well as a brief overview of other popular tools such as Expectation-Maximisation and linear classification (e.g. wide-margin Support Vector Machines).

For lecture slides / handouts - go to Lectures tab.

Required text:

Recommended reading:

• Ch. M. Bishop Pattern Recognition and Machine Learning, Springer, 2006.
• T. Hastie, R. Tibshirani, J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2001.