Following a discussion of machine learning methods and applications, to provide an overview of the general field of Bioinspired Artificial Intelligence and Robotics. Introduce students to the fundamental components of Evolutionary Computation and Humanoid Robotics. This module will introduce students to Evolutionary Algorithms in general and then delve into specific algorithms and tasks such as Genetic Programming, Grammatical Evolution, Evolutionary Robotics, Bioinspired robotics, and Evolutionary Humanoid Robotics.

1. Learning vs. Intelligence; 2. Evolutionary Algorithms; 3. Genetic Programming; 4. Grammatical Evolution; 5. Evolution Strategies; 6. Ant Colony Optimization; 7. Advanced Selection Methods; 8. Fitness functions; 9. Bioinspired robotics; 10. Evolutionary robotics; 11. Humanoid robotics; 12. Evolutionary Humanoid Robotics; 13. Societal Implications

On completion of this module students will be able to: 1. Understand the difference between concepts such as intelligence and learning, and strong and weak AI; 2. Recognise the fundamentals concepts underlying all Evolutionary Algorithms; 3. Describe the differences between various Evolutionary Algorithms; 4. Understand how to choose which Evolutionary Algorithm based on the application; 5. Know how to modify various Evolutionary Algorithms to function better when they fail to perform; 6. Understand the basic principles of Evolutionary Robotics, including its application to Humanoid Robotics.

1. Discuss the benefits (and potential downsides) of Machine Learning for industry and society

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The module is taught in the form of lectures and paper discussion groups in tutorials.