Andrew Philippides
Email:
andrewop AT sussex.ac.uk
The aim of this course is to provide the mathematical background needed to understand several subjects which appear in Informatics MSc courses. In particular, the course is a pre-requisite for students taking the 2nd term courses: Neural Networks and Computational Neuroscience.
Lectures:
Mondays 12-12.50 Chichester 3 3R241
Tuesdays 12-12.50 Chichester 3 3R143
Seminars:
Tuesdays 16-16.50 Chichester 3 Ci204/5
Topics in italics are likely to be used to illustrate the mathematical techniques. Not everything will be discussed at the same level of detail.
Course introduction.
General discussion of functions and notation.
Function examples.
Matrices and Vectors.
Network operations as matrices.
Matlab.
Programming networks in matlab.
Differential calculus, partial differentiation.
Gradient Descent.
Numerical methods for integration of differential equations.
Numerical integration of a model neuron
Dynamical systems analysis.
Analysis of GasNet neurons.
Probability and distributions.
Entropy and information theory.
Optimisation and introduction to hypothesis testing.
Analysis of data from A-life experiments.
The course is assessed by coursework only through a combination of weekly problem sheets and a min-project to be handed on Thursday in week 10 by 4pm. The project is to describe/explain a mathematical subject relevant to the courses you are undertaking in the rest of the course. Topics must be agreed with me.
Notes on some of the topics covered are available in HTML and in PDF. Further reading is suggested in the appropriate sections of the notes.
All content and materials copyright Andrew Philippides, 2005.