Undergraduate Course: Natural Computing (INFR09038)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 09 (Year 3 Undergraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
None |
|
|
| Course description |
This module teaches you about bio-inspired algorithms for optimisation and search problem. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search huge parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.
In this module, students will learn about
- The practicalities of natural computing methods: How to design algorithms for particular classes of problems.
- Some of the underlying theory: How such algorithms work and what is provable about them.
- Issues of experimental design: How to decide whether an metaheuristic algorithm works well.
- Current commercial applications.
- Current research directions. |
Entry Requirements
| Pre-requisites |
Students MUST have passed:
Mathematics for Informatics 4a (MATH08044) AND
Mathematics for Informatics 4b (MATH08045)
|
Co-requisites |
|
| Prohibited Combinations |
|
Other requirements |
Successful completion of Year 2 of an Informatics Single or Combined Degree, or equivalent by permission of the School. The course will involve a certain amount of mathematics in a some places, mainly basic probability and statistics.
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| Additional Costs |
None |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | 15:00 - 15:50 | | | | | Central | Lecture | | 1-11 | | | | | 15:00 - 15:50 |
| First Class |
Week 1, Tuesday, 15:00 - 15:50, Zone: Central. Lecture Theatre 1, Appleton Tower |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | 15:00 - 15:50 | | | | | Central | Lecture | | 1-11 | | | | | 15:00 - 15:50 |
| First Class |
Week 1, Tuesday, 15:00 - 15:50, Zone: Central. Lecture Theatre 1, Appleton Tower |
Summary of Intended Learning Outcomes
- Understanding of natural computation techniques and their broad applicability to a range of hard problems in search, optimisation and machine learning.
- To know when a natural computing technique is applicable, which one to choose and how to evaluate the results.
- To know how to apply a natural computing technique to a real problem and how to choose the parameters for optimal performance.
- Matching techniques with problems, evaluating results, tuning parameters, creating algorithms using inspiration from natural systems. |
Assessment Information
Written Examination 70 %;
Assessed Assignments 30 %
Assessment
The coursework is comprised of two homework assignments. The first assignment will be a computational study of an application of a bio-inspired algorithm (it will be worth 10%), the second assignment is worth 20% of the course mark and will be a comparison of two or more algorithms on a different problem. A six-page report and a MatLab program are to be prepared for each assignment. |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Marcelo Cintra
Tel: (0131 6)50 5118
Email: mc@inf.ed.ac.uk |
Course secretary |
Miss Tamise Totterdell
Tel: 0131 650 9970
Email: t.totterdell@ed.ac.uk |
|
copyright 2010 The University of Edinburgh -
1 September 2010 6:10 am
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