School of Electronics and Computer Science:
COMP3032 Intelligent Algorithms
Basic Information
| School | |
|---|---|
| Known as | COMP3032. |
| Session and Semester | Semester One, 2011 - 2012 |
| Credit | 10 Credit Points |
| Unit Leader | Prof Steve R Gunn |
| Teachers | Dr Srinandan Dasmahapatra |
| Moderators | Dr. Maria Polukarov |
| Study | 100 hours nominal |
| Assessment | Examination 70%, Coursework 30% |
| Coursework | 1 assignment |
| Teaching | 24 lectures |
| Referral | On referral, this unit will be assessed 100% by examination. |
| Syllabus Approved |
Description
Aims
- To introduce a set of intelligent algorithms that have found very broad applications in modern computer science.
- To develop theoretical and mathematical underpinnings of intelligent algorithms
- To show how these algorithms can be used in problem solving environments and understand their properties and limitations
- To gain experience with working with these algorithms particularly through the use of Matlab
Learning Outcomes
Knowledge and Understanding
Having successfully completed the module, you will be able to demonstrate knowledge and understanding of:
- The basic principles of linear algebra
- A broad range of optimisation techniques
- Probabilistic modelling methods
Intellectual Skills
Having successfully completed the module, you will be able to:
- Understand the underlying principles behind a range on intelligent algorithms
Practical Skills
Having successfully completed the module, you will be able to:
- Write, develop and debug MATLAB code for intelligent algorithms
- Choose, develop and adapt a wide range of algorithms for real world problems
General Transferable (key) Skills
Having successfully completed the module, you will be able to:
- Plan your work and keep to deadlines
- Using a variety of text sources evaluate and select a suitable algorithm for a given problem
Topics Covered
- Linear algebra
- solving sets of linear equations (conditioning)
- sub-spaces
- eigen-systems, PCA, Google
- Optimisation
- 1-D minimisation (Newton's method)
- Gradient descent in higher dimensions (problems of different scales)
- linear programming (duality, convexity)
- dynamic programming
- constrained optimisation (Lagrange multipliers)
- Probabilistic Modelling
- Maximum likelihood
- Hidden Markov Models
Teaching and learning activities
Teaching methods include
Two lectures per week are used to describe and develop the concepts and techniques listed above. In addition, there will be lab-based tutorials. Students are given supporting written material to illustrate the lectures.
Learning activities include
One piece of coursework is set which is an exercise in solving a real world problem using these techniques.
- Access to Departmental PC's with Matlab 6.1
- Matlab handbook provided.
- Printed notes available.
Methods of assessment
| Assessment method | Number | % contribution to final mark |
|---|---|---|
| Written Examination [exam] | 1 | 70 |
| Coursework Exercise [cwork] | 1 | 30 |
Feedback and student support during module study
- Written and verbal feedback will be given on the coursework assignment
- Lecturers are available for one to one discussions with students in the event of difficulties
Relationship between the teaching, learning and assessment methods and the planned learning outcomes
The examinations test the basic theoretical concepts of the subject and the main techniques used in numerical analysis. The function of the coursework is to test the students ability to tackle a real problem, produce working matlab code, and present believable results in a coherent way.
Resources
Background Resources
- Mark M. Meerschaert, Mathematical Model 2nd Edition,Academic Press 1998.
- Neil Gershenfeld, The Nature of Mathematical Modeling, CUP 1999.
Taught to
COMP3032
Non-existing cohort: "ceMEng3" (Optional)Pt III BSc Computer Science (Optional)
Non-existing cohort: "csBScAi3" (Optional)
Non-existing cohort: "csBScDs3" (Optional)
Non-existing cohort: "csBScIm3" (Optional)
Non-existing cohort: "csBSclm3" (Optional)
Pt III MEng Computer Science with Artificial Intelligence (Optional)
Pt III MEng Computer Science (Optional)
Pt III MEng Computer Science with Distributed Systems & Networks (Optional)
Pt III MEng Computer Science with Image and Multimedia Systems (Optional)
Pt III MEng Computer Science with Mobile and Secure Systems (Optional)
MSc in Artificial Intelligence (Compulsory)
Non-existing cohort: "csMScCo" (Optional)
MSc in Software Engineering (Optional)
Pt III BEng Electronic Engineering (Optional)
Pt III MEng Electronic Engineering (Optional)
Pt III MEng Electronic Engineering with Artificial Intelligence (Compulsory)
Pt III MEng Electronic Engineering with Computer Systems (Optional)
Pt III MEng Electronic Engineering with Nanotechnology (Optional)
Pt III MEng Electronic Engineering with Optical Communications (Optional)
Pt III MEng Electronic Engineering with Power Systems (Optional)
Pt III MEng Electronic Engineering with Mobile and Secure Systems (Optional)
Pt III MEng Electronic Engineering with Wireless Communications (Optional)
MSc in Systems and Signal Processing (Compulsory)
Pt III BEng Software Engineering (Optional)
Pt III MEng Software Engineering (Optional)
Students who are not registered on an ECS approved programme may take this module subject to meeting its pre-requisites and the availability of resources. To confirm this, please can you contact the module leader (as listed above) in the first instance. They will then refer you on to the appropriate director of studies for formal approval of your selection.