|
To help evaluate this course, please fill in the Course Questionnaire.
The mathematical systems studied can be presented as a conceptual basis for developing abstract theories, or as models of cognitive systems. Acquiring a sound grasp of the interplay between these two perspectives is an important foundation for cognitive science.
The course is mainly targeted at students on the MA in Cognitive Science, but it is suitable also for other first year students in Informatics.
For more information, please see the Official Course Descriptor for FMCS1.
Steve Renals; office: Room 5BP-2L04, 5 Buccleuch Place; email: srenals@inf.ed.ac.uk
Mark van Rossum; office: Room D7, 5 Forrest Hill; email: mvanross@inf.ed.ac.uk; phone: 511211.
| Mondays | 16:10-17:00 | Room 8.16, David Hume Tower |
| Tuesdays | 16:10-17:00 | Room 8.16, David Hume Tower |
| Thursdays | 16:10-17:00 | Room 8.16, David Hume Tower |
| Mondays | 13:00-13:50 | Room 5.01, Appleton Tower |
Tutorials take place every week, starting in week 2.
| Wednesdays | 12:10-14:00 | Computer Lab North, Level 5, Appleton Tower | |
| Thursdays | 13:00-14:50 | Computer Lab North, Level 5, Appleton Tower |
There are five lab sessions, every other week, starting in week 2 (see schedule below).
If you have specific questions, please email the teaching assistants or the course organiser directly.
The lecturers and TAs will post announcements regarding the course to the course mailing list. All students taking the course are automatically subscribed to this list. Previous postings can be accessed using the mailing list archive.
| David Barber's Introduction to Matlab |
| Kermit Sigmon's Matlab Primer |
| No. | Issued | Due | Exercises | Materials | Solutions |
| 1 | Jan 25 | Feb 9 | Vectors and Matrices | - | |
| 2 | Feb 14 | Mar 2 | Convolution | - | |
| 3 | Mar 6 | Mar 16 | Probability | - | Solutions |
| 4 | Mar 12 | Mar 23 | Probability and Information Theory | - |
All assignments are due at 16:00 on the due date, and are to be handed in as hardcopies at the Informatics Teaching Office (Appleton Tower, 5th floor).
This course will be examined at the end of the semester with a 120 min closed-book exam (for the exam date, please check here). For exam preparation, students should refer to the past exam papers for this course:
| Practice Exam | Exam Paper | Solutions |
| 2004/2005 | Exam Paper | Solutions |
| 2004/2005 Resit | Exam Paper | Solutions |
Please read the information regarding the examination, assessed asignments, and regulations for this course.
| No. | Date | Lecturer | Topic | Lecture Notes | Reading |
| 1 | 08 Jan | Van Rossum | Vectors | Slides, Ch. 1.0-1.4 | Greenberg Ch. 9.1-9.6 |
| 2 | 09 Jan | Van Rossum | Matrices | Ch. 1.5-2.1 | Greenberg Ch. 10.1, 10.2 |
| 3 | 11 Jan | Van Rossum | Matrices | Ch. 2.1-2.5 | Greenberg Ch. 10.3 |
| 4 | 15 Jan | Van Rossum | Matrices | Ch. 2.6, 3 | Greenberg Ch. 10.4, 10.6, 10.8 |
| 5 | 16 Jan | Van Rossum | Perceptron | Ch. 3 | Hertz et al. Ch. 5.1, 5.2, 5.4 |
| 6 | 18 Jan | Van Rossum | Perceptron | Ch. 3 | - |
| 7 | 22 Jan | Van Rossum | Differentiation | Ch. 4 | Greenberg Ch. 13.3-5, 13.7 |
| 8 | 23 Jan | Van Rossum | Backpropagation | Ch. 5 | Hertz et al. Ch. 6.1, 6.3 |
| 9 | 25 Jan | Van Rossum | Backpropagation | Ch. 5 | - |
| 10 | 29 Jan | Van Rossum | Filters | Ch. 6 | Greenberg Ch. 21.1-4 |
| 11 | 30 Jan | Van Rossum | Filters | Ch. 6 | - |
| 12 | 01 Feb | Van Rossum | Filters | Ch. 6 | - |
| - | 05 Feb | Van Rossum | NO LECTURE | - | |
| - | 06 Feb | Van Rossum | NO LECTURE | - | |
| 13 | 08 Feb | Van Rossum | Differential Equations | Ch. 7 | Greenberg Ch. 1 |
| 14 | 12 Feb | Van Rossum | Differential Equations | Ch. 7 | - |
| 15 | 13 Feb | Van Rossum | Phase Plane | Ch. 8 | Greenberg Ch. 7.1-7.4 |
| 16 | 15 Feb | Renals | Introduction to Probability Theory; Combinatorial Methods | Slides (4-up) | Miller and Miller Ch. 1 |
| 17 | 19 Feb | Renals | Sample Spaces, Events, Probabilities | Slides (4-up) | Miller and Miller Ch. 2 |
| 18 | 20 Feb | Renals | Conditional Probability; Bayes' Theorem | Slides (4-up) | Miller and Miller Ch. 2 |
| 19 | 22 Feb | Renals | Application of Bayes' Theorem; Discrete Random Variables; Distributions | Slides (4-up) | Miller and Miller Ch. 3 |
| 20 | 26 Feb | Renals | Joint, Marginal, and Conditional Distributions | Slides (4-up) | Miller and Miller Ch. 3 |
| 21 | 27 Feb | Renals | Continuous Random Variables; Densities | Slides (4-up) | Miller and Miller Ch. 3 |
| 22 | 01 Mar | Renals | Expectation and Variance; Chebyshev's Theorem | Slides (4-up) | Miller and Miller Ch. 4 |
| 23 | 05 Mar | Renals | Special Distributions and Densities | Slides (4-up) | Miller and Miller Ch. 5, 6 |
| 24 | 06 Mar | Osborne | Application: Bayesian Cognitive Science | Slides | - |
| 25 | 08 Mar | Osborne | Entropy, Joint Entropy, Conditional Entropy | Slides | Manning and Schütze, Ch 2.2 |
| 26 | 12 Mar | Osborne | Entropy Rate; Mutual Information | Slides | Manning and Schütze, Ch 2.2 |
| 27 | 13 Mar | Osborne | Application of Mutual Information; Codes | Slides | Manning and Schütze, Ch 2.2 |
| 28 | 15 Mar | Osborne | Kraft Inequality; Source Coding Theorem; Huffman Coding | Slides | Manning and Schütze, Ch 2.2 |
| 29 | 19 Mar | Osborne | Noisy Channel Model and Applications; Kullback-Leibler Divergence; Cross-entropy | Slides | Manning and Schütze, Ch 2.2 |
| 30 | 20 Mar | Osborne | Revision | - | - |
| No. | Tutor | Exercises | Solutions | |
| 1 | Renals | Lecture Notes, Ch. 1, ex. 1-12 | ||
| 2 | Renals | Lecture Notes, Ch. 2, ex. 1-10 | ||
| 3 | Renals | Lecture Notes, Ch. 3, ex. 1-3, Ch. 4, ex. 1-6 | ||
| 4 | Renals | Lecture Notes, Ch. 6, ex. 1-3, 5 | ||
| 5 | Renals | Lecture Notes, Ch 7, ex. 1, 2, Ch. 8, ex. 1, 2 | ||
| 6 | Renals | Combinatorics, Basic Probability, Bayes' Theorem | Solutions | |
| 7 | Renals | Random Variables and Probability Distributions | Solutions | |
| 8 | Renals | Expectation and Variance; Special Distributions | Solutions | |
| 9 | Renals | Entropy; Mutual Information | Solutions | |
| 10 | Renals | Codes; KL Divergence; Noisy Channel Model | Solutions |
| No. | Demostrator | Exercises | Solutions | |
| 1 | Bell | Matlab and Vectors and Matrices | ||
| 2 | Bell | Perceptrons | ||
| 3 | Bell | Dynamics of Simple Neuron Populations | ||
| 4 | Bell | Probability Theory | Solutions | |
| 5 | Bell | Information Theory | Solutions |
|
Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK
Tel: +44 131 651 5661, Fax: +44 131 651 1426, E-mail: school-office@inf.ed.ac.uk Please contact our webadmin with any comments or corrections. Logging and Cookies Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh |
