By the end of the module the student should be able to:

- Describe the problem of supervised learning from the point of view of function approximation, optimization, and statistics.

- Identify the most suitable optimization and modelling approach for a given machine learning problem.

- Analyse the performance of various optimization algorithms from the point of view of computational complexity (both space and time) and statistical accuracy.

- Implement a simple neural network architecture and apply it to a pattern recognition task.

Material will be selected from the following topics in Optimisation and Machine Learning

Optimisation

- Basic algorithms (gradient descent, Newton’s method)

- Convexity, Lagrange duality and KKT theory

- Quadratic optimization and support vector machines

- Subgradients and nonsmooth analysis

- Proximal gradient methods

- Accelerated and stochastic algorithms

Machine learning

- Neural networks and deep learning

- Stochastic gradient descent

- Kernel methods and Gaussian processes

- Recurrent neural networks

- Applications (pattern recognition, time series prediction)

It is recommended that students should be familiar with material in vecter integral and differential calculus

• Group/class feedback, post-assessment

Not yet recorded.