Module overview
Machine Learning is about extracting useful information from large and complex datasets. Although driven by applications, the techniques used are based on a broad mathematical basis. This course provides the mathematical foundations of the subject from functional analysis through to optimisation, convexity and information theory.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Derive learning algorithms with constraints and demonstrate proficiency in techniques including the method of Langrange Multipliers and the utilisation of duality.
- Derive original machine learning algorithms from first principles.
- Choose appropriate learning algorithms for particular tasks
- Formulate machine learning methods that capture the features present in the problem.
- Read and understand the research literature in machine learning.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- A broad range of mathematical methods that underpin modern machine learning.
- A number of the leading machine learning methods that are built on mathematical foundations.
- What makes machine learning work well including knowledge of basic learning theory.
Syllabus
The module will introduce a number a mathematical topics including
- functional analysis (vector spaces, norms, inner-products, etc.)
- unconstrained and constrained optimisation (including duality)
- convexity
- probabilistic inference
- information theory
within the context of machine learning methods such as
- ensemble learning (bagging, random forest and boosting)
- support vector machines
- Gaussian process.
In addition the course will cover learning theory with notions such as bias variance and an understanding of overfitting, inductive bias, etc.
Learning and Teaching
Teaching and learning methods
The module consists of:
- Lectures
- Tutorials
| Type | Hours |
|---|---|
| Tutorial | 12 |
| Lecture | 36 |
| Revision | 32 |
| Wider reading or practice | 20 |
| Guided independent study | 50 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Examination | 85% |
| Worksheet | 15% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Examination | 100% |
Repeat Information
Repeat type: Internal & External