Basic Machine Learning Algorithms

Monday 6 March

This lecture will present a set of fundamental Machine Learning algorithms and software tools for their easy application to data analysis. The lecture targets both physicists and computer scientists. It will rely on some of the background information presented in the Future Selection and Statistical Learning Basics lecture of this series.

The lecture will address the following issues:

What are and how to build decision trees?

Short description of the idea, method of choosing correct test, advantages and

disadvantages of decision trees.

Random forest as a variation of decision tree algorithm

Why and when random forest are better than a single tree. What do we loose in

comparison to decision trees?

Main ideas of "lazy learning"

Nearest neighbour algorithm and its generalization - kNN as examples of lazy

algorithms. They provide very good results in classification with minimum effort.

Grouping algorithms - when to use them

Presentation of cobweb algorithm.

R-language - open source environment for development and testing

Some of the algorithms described in the presentation will be presented in
R-environment.