AI AND AI PROGRAMMING - 2017/8

Module code: EEEM005

Module provider

Electrical and Electronic Engineering

Module Leader

WELLS K Dr (Elec Elec En)

Number of Credits

15

ECT Credits

7.5

Framework

FHEQ Level 7

JACs code

I400

Module cap (Maximum number of students)

N/A

Module Availability

Semester 2

Overall student workload

Independent Study Hours: 107

Lecture Hours: 33

Laboratory Hours: 10

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework LAB AND ASSIGNMENT 25%
Examination EXAMINATION - 2HRS 75%

Alternative Assessment

Neural Network coursework Essay

Prerequisites / Co-requisites

None specifically advised.

Module overview

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems.

Module aims

Deliver knowledge on the basic ideas and concepts that underlie the development of artificially intelligent machine systems.

Learning outcomes

Attributes Developed
Demonstrate knowledge of the set of methods which would be needed to develop an intelligent system. K
Demonstrate an appreciation of the advantages and limitations of these different methods. KC
Demonstrate ability to apply these methods, and so propose suitable solutions to different problem domains in which an intelligent system can provide useful functionality to aide human activity in solving ‘real world' problems. KCT
Demonstrate ability to implement Prolog programs to solve some of these tasks KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes:

Lecturer  KW

15 Lecture hours including 3 problem classes covering:

Historical Overview - Definition of artificial intelligence (AI).Application areas. General problem solving versus specific knowledge. Complexity.

Heuristic Search - Uninformed versus informed search strategies. Formal properties of A*. Minimax game search, alpha-beta pruning.

Logic and Resolution - Knowledge representation. Propositional and predicate calculus. Inference rules. Clause form. Resolution strategies. Prolog and logic programming.

Uncertainty Reasoning - Probabilistic reasoning and Bayes theorem. Belief networks. Dempster-Shafer theory. Fuzzy logic.

Lecturer  TW

15 lecture hours (approx. 5 hiurs programming and 10 hours Neural Networks) with interspersed problem classes covering:

Basic Prolog - execution model, declarative and procedural meaning, backtracking, arithmetic, list representation, negation as failure and difficulties, simple examples.

Prolog Programming and Techniques - input/output, meta-logical and extra-logical predicates, set predicates, cuts, program development and style, correctness and completeness, Applications

    Multi-Layer Perceptrons - Convergence theorem, non-separability, LMS algorithms,    steepest  descent, back-propagation, generalisation, learning factors.

    Radial Basis Function Networks - Multivariable interpolation, regularisation,  comparison with MLP, learning strategies.

    Self-Organising Systems - Hebbian learning, competitive learning, SOFM, LVQ

              Recurrent networks - energy functions, Hopfield net, nonlinear dynamical systems, Liapunov stability, attractors

Methods of Teaching / Learning

The learning and teaching strategy is designed to achieve the aforementioned  learning outcomes.

 

The learning and teaching methods include:

Lectures: 30 hours over 10 weeks, 3 hours per week

Labs: 4 supervised labs teaching basic Prolog skills

Assignment: Programming assignment

Alternative assignment: Neural Network report 

 

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate subject-specific knowledge and how this knowledge can be applied in the design of AI solutions to real world problems.

Thus, the summative assessment for this module consists of:

 

Prolog Assignment coursework          (25%) (address learning outcomes 1-4)

Examination (2Hrs)                             (75%) (address learning outcomes 1-3)

 

Formative assessment

is provided via feedback gained from the problem classes which take place during scheduled lecture sessions, which can be used to prepare for the summative assessment.

 

Reading list

Reading list for AI AND AI PROGRAMMING : http://aspire.surrey.ac.uk/modules/eeem005

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.