MSc Machine Learning

Machine Learning (ML) is a subfield of AI that focuses on fitting mathematical functions to data for practical applications such as predictive modelling. It is concerned with creating mathematical "data structures" that allow a computer to exhibit behaviour that would normally be considered human. Typical applications include spam filtering, speech recognition, medical diagnosis and weather prediction. The data structures we use (known as "models") come in various forms, e.g. trees, graphs, algebraic equations and probability distributions. The emphasis is on constructing these models automatically from data – for example, making a weather predictor from a data file of historical weather patterns.

Introductory courses on Linear Algebra, Probability, Calculus and Python Programming.

Machine learning is now likely the most prominent branch of Artificial Intelligence (AI) and currently underpins the wide societal interest in AI as a general-purpose technology. This course aims to introduce the concepts behind various Machine Learning techniques, including how they work, and to use existing software packages to illustrate how they behave.

1. Describe the fundamental concepts of machine learning.

2. Describe the advantages and disadvantages of linear and non-linear approaches to supervised learning.

3. Explain the models and algorithms involved in a pipeline for supervised learning.

4. Use basic concepts of linear algebra to write scalar objective functions in terms of vector and matrix operations.

5. Design, develop, and evaluate specific predictive models for data-driven applications using tools such as Scikit-learn and automatic differentiation frameworks.

6. Critically assess technologies and analyse their suitability for specific application scenarios.

1. Introduction to Machine Learning

2. An end-to-end Machine Learning project

3. Linear models for regression and classification

4. Neural Networks and Convolutional Neural Networks

5. Sequential models: Language models, Recurrent Neural Networks and Transformers.

- Asynchronous material in the form of video lectures, formative exercises, lecture slides and code examples delivered via the virtual learning environment, lectures and supported tutorials.

- Weekly lectures consolidating asynchronous materials and providing opportunities for discussion and questions.

- Bi-weekly supervised lab sessions and tutorials that provide support for coursework and formative exercises.

Individual and general feedback for the Practical skills assessment.

Formative assessment in Lectures and Practical sessions.

Simon J.D. Prince, Understanding Deep Learning, MIT Press, 2023.

Christopher Bishop, Deep Learning: Foundations and Concepts, Springer, 2023.

Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras and Tensor-Flow, O’Reilly, 3rd Edition, 2022.

Moritz Hardt and Benjamin Recht, Patterns, Predictions, and Actions: Foundations of Machine Learning, Princeton University Press, 2022.

Kevin Murphy, Probabilistic Machine Learning: an Introduction, First edition, The MIT Press, 2022.

Simon Rogers and Mark Girolami, A First Course in Machine Learning, Chapman and Hall/CRC Press, 2nd Edition, 2016.

Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.