THIS COURSE IS NO LONGER GOING TO BE RUN BY DAVID

This course intends to provide an introduction to machine learning techniques. The course includes discussion on the theory and ideas behind various modern algorithms, so basic knowledge of probability and statistics is required. This is also a focus on practical experience of using suitable toolkits, available in Python, on various datasets.

There is a high demand for computational modellers and data scientists and this course aims to teach skills suitable for careers in a wide range of public and private institutions.

On completion of this course, students will be able to:

• Explain the operation of different supervised and unsupervised algorithms and their practical uses.
• Select and apply a suitable learning algorithm to a range of basic problems.
• Use a suitable programming language to work with data and apply machine learning tools to it.
• Interpret the output and validity of a learning algorithm.

Week	Topic	Lecturer
1	Introduction	David
2	Data and Features	David
3	Dimension Reduction	Jacob
4	Distance-Based Models	???
5	Linear Regression	???
6	Non-Linear Regression	???
7	“Classical” Classification	David/???
8	Ensemble Classification	David/Jacob
9	Neural Networks pt.1	Jacob
10	Neural Networks pt.2	Jacob
11	Ethics	David

This project is maintained by mlp-s2-22

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