Machine Learning and Data Science - 2019 entry

This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.

Pre-requisites: COM1011 Fundamentals of Machine Learning, ECM1400, ECM1410, MTH1002, MTH1004, or equivalent

Co-requisites: MTH2006

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

This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.

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

Module Specific Skills and Knowledge:

1 Analyse a broad range of data science problems, design models and write programs to solve them;

2 Utilise a range of supervised and unsupervised pattern recognition and machine learning techniques to solve a variety of problems;

Discipline Specific Skills and Knowledge:

3 State the challenges and limitations entailed by various machine learning approaches;

4 Propose the most suited analysis tools for specific data and problems;

Personal and Key Transferable/ Employment Skills and Knowledge:

5 Identify the compromises and trade-offs that must be made when translating theory into practice;

6 Critically read and report on research papers. 

Outline of the topics covered in this module:

• Gradient-based optimisation;

• Error and loss functions;

• Decision Trees and Random Forests;

• Ensemble methods;

• PCA;

• Deep Neural Networks, convolutional architectures;

• Support Vector Machines and large margin classification;

• Dimension reduction (forward & backward elimination).

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: