»Ê¼Ò»ªÈË

XClose

UCL Module Catalogue

Home
Menu

Artificial Intelligence and Neural Computing (COMP0024)

Key information

Faculty
Faculty of Engineering Sciences
Teaching department
Computer Science
Credit value
15
Restrictions
Module delivery for UG (FHEQ Level 6) available on BSc Computer Science; MEng Computer Science; MEng Mathematical Computation; BASc Science and Engineering. Module delivery for PGT (FHEQ Level 7) available on MSc Computer Science (subject to pre-selection approval by the Module Leader.)
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

Aims:

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.

Intended learning outcomes:

On successful completion of the module, a student will be able to:

  1. 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.
  2. Identify problems that can be expressed in terms of neural networks, and to select an appropriate learning methodology for the problem area.

Indicative content:

The following are indicative of the topics the module will typically cover:

Artificial intelligence:

  • Nature of artificial intelligence.
  • Searching state spaces.
  • Utility theory.
  • Logic for artificial intelligence.
  • Argumentation.
  • Reasoning about concepts.
  • Reasoning about uncertainty.
  • Machine learning.
  • Common-sense reasoning.

Neural Computing:

  • Overview of network architectures and learning paradigms.
  • Fully connected networks.
  • Convolutional Neural Networks.
  • Networks Dealing with Sequential Data.
  • (Deep) Reinforcement Learning.
  • Meta-Learning.

Requisites:

To be eligible to select the module delivery for Undergraduate (FHEQ Level 6) as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; and (2) have passed a module in propositional and predicate logic at FHEQ level 4 or higher.

To be eligible to select the module delivery for Postgraduate (FHEQ Level 7) as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have a bachelor’s degree or higher in mathematics or philosophy; (3) have passed a module in propositional and predicate logic at FHEQ level 4 or higher; and (4) have pre-approval to select the module from the Module leader.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
90% Exam
10% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
12
Module leader
Professor Anthony Hunter
Who to contact for more information
cs.pgt-students@ucl.ac.uk

Intended teaching term: Term 2 ÌýÌýÌý Undergraduate (FHEQ Level 6)

Teaching and assessment

Mode of study
In person
Methods of assessment
90% Exam
10% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
37
Module leader
Professor Anthony Hunter
Who to contact for more information
cs.pgt-students@ucl.ac.uk

Last updated

This module description was last updated on 19th August 2024.

Ìý