Module overview
Machine Learning is about extracting useful information from large and complex datasets. Building upon the Machine Learning (I) module, students will learn about a broader range of learning tasks. There will be significant exposure to solving real-world machine learning tasks.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Gain a broad understanding of how to choose appropriate learning algorithms for particular tasks
- Be able to derive learning algorithms with constraints and demonstrate proficiency in techniques including the method of Langrange Multipliers and the utilisation of duality.
- Be able to formulate simple latent variable models
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Gain facility in choosing appropriate approaches to cleaning, preprocessing and normalising data
- Systematically approach real world machine learning problems
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Be able to articulate the relationship between different machine learning techniques and different types of problem
- Demonstrate knowledge of a wider range of learning tasks including matrix completion and generative modelling
Syllabus
How to approach solving real world problems
- Data quality, cleansing, preparation
- Choosing the right approach
More learning problems:
- Matrix completion
- Sequence modelling and temporal prediction
- Introduction to language modelling
- Obtaining latent representations (e.g. dimensionality reduction)
- Generative modelling
Theory:
- Overfitting
- The bias-variance dilemma
- Convexity
- Constrained optimisation & Lagrange multipliers
- Duality
Techniques:
- Mixture modelling
- SVMs and the kernel trick
- Deeper neural networks and different types of layers & inductive biases (e.g. weight sharing - convolutional, local, etc; sparsity)
- NNMF
- Introduction to Probabilistic Graphical Models + associated algorithms
- Ensemble methods
Learning and Teaching
Teaching and learning methods
The module consists of:
- Lectures
- Combined tutorials and computing laboratory sessions
Study time
| Type | Hours |
|---|---|
| Specialist Laboratory | 8 |
| Wider reading or practice | 43 |
| Revision | 10 |
| Lecture | 36 |
| Completion of assessment task | 45 |
| Tutorial | 8 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Bishop, Christopher M.. Pattern Recognition and Machine Learning.
Mackay, David J. C.. Information Theory, Inference and Learning Algorithms..
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Breakdown
| Method | Percentage contribution |
|---|---|
| CAA Exam | 70% |
| Group project | 30% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Breakdown
| Method | Percentage contribution |
|---|---|
| CAA Exam | 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.
Breakdown
| Method | Percentage contribution |
|---|---|
| CAA Exam | 100% |
Repeat Information
Repeat type: Internal & External