Machine Learning And Bioinspired Optimisation


In this module we focus on learning agents that interact with an initially unknown world. Since the world is dynamic this module will put strong emphasis on learning to deal with sequential data unlike many other machine learning courses. The aims can be summarised as:
To introduce and give an overview to state of the art bio-inspired self-adapting methods. 
To enable students to not only learn to build models with reactive input/output mappings but also build computer programs that sense and perceive their environment, plan, and make optimal decisions. 
To familiarise students with multi-agent reinforcement learning, swarm intelligence, deep neural networks, evolutionary game theory, artificial immune systems and DNA computing.
To demonstrate principles of bio-inspired methods, provide indicative examples, develop problem-solving abilities and provide students with experience to apply the learnt methods in real-world problems.


This module will cover the following topics: Introduction to parallel problem solving from nature/overview (2 lectures) Reinforcement Learning/multi-agent reinforcement learning/replicator dynamics (8 lectures) Swarm Intelligence: Ant System, Ant Colony Optimization/Bee System/Swarm Robotics (6 lectures) Deep Learning: Restricted Boltzman Machines/auto-encoder networks/deep belief networks (8 lectures) Artificial immune systems (4 lectures) DNA computing (2 lectures) Lecture slides and reading material will be made available to the students.

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.

Learning Outcomes

(LO1) A systematic understanding of bio-inspired algorithms that can be used for autonomous agent design and complex optimisation problems.

(LO2) In depth insight in  the mathematics of biologically inspired machine learning and optimisation methods.

(LO3) A comprehensive understanding of the benefits and drawbacks of the various methods.

(LO4) Demonstrate knowledge of using the methods in real-world applications (e.g. logistic problems).

(LO5) Practical assignments will lead to hands on experience using tools as well as coding of own algorithms.

Learning Strategy

Teaching Method 1 - lectures
Description: students will be expected to attend three hours of formal lectures in a typical week
Attendance Recorded: Yes

Teaching Method 2 - tutorials
Description: one hour of weekly seminar given by students in groups, or one hour of tutorial by instructor.
Attendance Recorded: Yes