Computational Machine Learning and Optimisation

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• demonstrate learning and understanding in major tools and methods in the area of machine learning.
• demonstrate knowledge and understanding of nonlinear programming modelling techniques
• demonstrate knowledge and understanding of nonlinear programming solution algorithms

Fundamentals of machine learning: random forests, neural networks, training tools for machine learning such as stochastic gradients methods, and coordinates and proximal descent methods.

Machine learning packages used in practice.

The basics of nonlinear optimization: constrained and unconstrained optimization problems. Optimality criteria. Modelling. Applications of nonlinear optimization in finance, business, engineering, and machine learning. Algorithms for solving constrained and unconstrained problems, e.g. direct search methods, steepest descent, Newton's method, penalty and barrier methods.

Teaching and learning methods

Twenty 1-hour lectures

Four 1-hour computer sessions

Resources & Reading list

Textbooks

Numerical Optimization (1999). Nocedal & Stephen Wright. Springer Verlag.

MS Bazaraa, CM Shetty & HD Sherali (1994). Non-linear Programming: Theory and Applications. Wiley.

DP Bertsekas (2004). Non-linear Programming. Athena Scientific.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

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.

Repeat Information

Repeat type: Internal & External