Fundamentals of Machine Learning - 2019 entry

Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.

Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Understand and identify the compromises and trade-offs that must be made when using a machine learning approach;

2 Analyse problems from a data-centric point of view, choose among a range of supervised and unsupervised machine learning techniques and use relevant software libraries to solve them;

Discipline Specific Skills and Knowledge:

3 State the importance and difficulty of establishing machine learning solutions;

4 Use a modern programming language for scientific analysis and simulation;

Personal and Key Transferable/ Employment Skills and Knowledge:

5 Identify the compromises that must be made when translating theory into practice;

6 Critically read and report on research papers.

- Introductory Material: history of Artificial Intelligence and Machine Learning;

- Data: the nature of data, how to represent data sources: text, sound, images, networks;

- AI and ML applications to real world cases;

- Data Representation: feature selection, feature construction;

- Machine Learning Paradigms: supervised, unsupervised, reinforcement learning;

- Error Measures for Different Machine Learning Tasks: classification, regression, ranking, clustering;

- Algorithms: k-nearest neighbours, linear models, naïve Bayes, k-means, neural networks;

- Theoretical Notions in Machine Learning: model capacity and overfitting, curse of dimensionality.

Referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Basic Reading:

ELE: http://vle.exeter.ac.uk

Reading list for this module: