I teach the modules Adaptive Intelligence and Modelling and Simulations of Natural Systems to 3rd year undergraduate and MSc students. The relevant material is available on MOLE.


COM3240/COM6106 Adaptive Intelligence

This module introduces the topic of bioinspired Machine Learning and in particular Unsupervised and Reinforcement Learning in Artificial Neural Networks.

Prerequisites

In addition to excellent programming skills, Matrix Algebra and very good knowledge of Calculus (derivatives) is required for this module. Either Python or Matlab knowledge is also required, as they will be used for the laboratory exercises. In addition, the module requires self-study of the provided material and any additional revision material one may need,  in order to get an in depth understanding of the theory underlining these techniques. 

Evaluation 

Assignments (40%) and a formal exam (60%). The 15 credit version has additional assignment components.

Bibliography

The reading material for this module includes a selection from the books: "Introduction to the Theory of Neural Computation" by Hertz, Krogh & Palmer, “ Neural Dynamics" by Gerstner & colleagues, "Theoretical Neuroscience" by Dayan & Abbot and "Reinforcement Learning: An Introduction" by Sutton & Barto.


COM3001/6009 Modelling and Simulation of Natural Systems

This module introduces basic mathematical modelling and agent based modelling techniques applied on natural systems. 

Prerequisites

The module requires that students already have very good programming skills (and some experience with Matlab) and at least A-level mathematics background. It also requires self-study of the provided material and any additional revision material one may need, e.g. calculus, in order to get an in depth understanding of these techniques. 

Evaluation

10 credit version (COM3001): Assignment (40%) and a formal exam (60%). 15 credit version (COM6009): Assignments (65%) and a formal exam (35%).

Bibliography

For the Mathematical Modelling part of the course: (i) A selection from Chapters 1 and 11, "Differential Equations, Dynamical Systems & An Introduction to Chaos", by Hirsch, Smale and Devaney. (ii) Chapter 20 from "Applied Numerical Methods with Matlab for Engineers and Scientists" by Chapra. (iii) Chapter 1 and sections 4.1, 4.2 from "Spiking Neuron Models" by Gerstner & Kistler.

© Eleni Vasilaki 2018