Study Guide

Objective

By the end of the module, students should be able to:
- Develop an appreciation for what is involved in machine learning (data mining) from data
- Understand a wide variety of learning algorithms
- Understand how to evaluate models generated from data
- Apply the algorithms to solve real problems, optimize the models learned and report on the expected performance

Transferable skills:
- Mathematical analysis of learning methods.
- Evaluation of algorithms.
- Programming skills in Python

Content

This course aims to provide students with an in-depth introduction to the main topics of Machine Learning.

It will cover some of the main models and algorithms for regression, classification and clustering. Topics such as linear and logistic regression, classification trees, rules, SVMs, neural networks, clustering, feature selection and dimensionality reduction. Visualisation and evaluation of machine
learning models.

Materials

Bibliography
Jake VanderPlas. Python Data Science Handbook,
https://jakevdp.github.io/PythonDataScienceHandbook/
Ian Witten, Eibe Frank, Mark Hall and Chris Pal, Data Mining: Practical Machine Learning Tools
and Techniques, 4th Edt, 2016

Other bibliography
Mitchell T, Machine Learning, McGraw-Hill, 1997
S. Rogers and M. Girolami, A first course in Machine Learning, CRC Press, 2011
C. Bishop, Pattern Recognition and Machine Learning, 2007
D. Barber, Bayesian Reasoning and Machine Learning, 2012

Other online references
https://www.w3schools.com/python/python_ml_getting_started.asp
https://github.com/rasbt/python-machine-learning-book-3rd-edition

Further information

Students should bring their own laptop.

Evaluation scale

0-5

Prerequisites

The course will use Python and/or R programming languages.
Some familiarity with linear algebra, probability theory.