COMP0024 Artificial Intelligence and Neural Computing
This database contains the 2018-19 versions of syllabuses.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
Artificial Intelligence and Neural Computing
This module introduces artificial intelligence and neural computing as both technical subjects and as fields of intellectual activity. The overall aims are:
- to introduce basic concepts of artificial intelligence for reasoning and learning behaviour ; and
- to introduce neural computing as an alternative knowledge acquisition/representation paradigm, to explain its basic principles and to describe a range of neural computing techniques and their application areas.
On successful completion of the module, a student will be able to:
- identify problems that can be expressed in terms of search problems or logic problems, and translate them into the appropriate form, and know how they could be addressed using an algorithmic approach;
- identify problems that can be expressed in terms of neural networks, and to select an appropriate learning methodology for the problem area.
Availability and prerequisites
This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.
In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.
Programmes on which available:
In order to be eligible to select this module, students must have:
- Nature of artificial intelligence
- Searching state spaces
- Utility theory
- Logic for artificial intelligence
- Reasoning about concepts
- Reasoning about uncertainty
- Machine learning
- Overview of network architectures and learning paradigms.
- Binary decision neurons.
- Single-layer perceptrons and their limitations.
- Multilayer perceptrons.
- The Hopfield model.
- Reinforcement learning
- The Kohonen self-organising feature map.
An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.
The module is delivered through lectures and two written courseworks; there are no programming exercises.
This module delivery is assessed as below:
Written examination (2hrs 30mins)
In order to pass this module delivery, students must:
- achieve an overall weighted module mark of at least 40%; and
- achieve a mark of at least 30% in any components of assessment weighed ≥ 30% of the module.
Where a component comprises multiple assessments, the minimum mark applies to the overall component.