AI AND AI PROGRAMMING - 2021/2
Module code: EEEM005
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
These changes include the implementation of a hybrid teaching approach during 2020/21. Detailed information on all changes is available at: https://www.surrey.ac.uk/coronavirus/course-changes. This webpage sets out information relating to general University changes, and will also direct you to consider additional specific information relating to your chosen programme.
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This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems.
Electrical and Electronic Engineering
WELLS Kevin (Elec Elec En)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I400
Module cap (Maximum number of students): 109
Prerequisites / Co-requisites
None specifically advised.
Indicative content includes:
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.
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
|Assessment type||Unit of assessment||Weighting|
|Examination||EXAMINATION - 2HRS||75|
Neural Network coursework Essay
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/NN Assignment coursework (25%) (address learning outcomes 1-4)
Examination (2Hrs) (75%) (address learning outcomes 1-3)
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.
- Deliver knowledge on the basic ideas and concepts that underlie the development of artificially intelligent machine systems.
|1||Demonstrate knowledge of the set of methods which would be needed to develop an intelligent system.||K|
|2||Demonstrate an appreciation of the advantages and limitations of these different methods.||KC|
|3||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|
|4||Demonstrate ability to implement Prolog programs to solve some of these tasks||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 107
Lecture Hours: 33
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
Assignment: Programming assignment
Alternative assignment: Neural Network report
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: EEEM005
Programmes this module appears in
|Electronic Engineering MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Artificial Intelligence MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Computer and Internet Engineering MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering with Computer Systems MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering MEng||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Electronic Engineering with Professional Postgraduate Year MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|FinTech and Policy MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Medical Imaging MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
|Data Science MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Computer Vision, Robotics and Machine Learning MSc||2||Compulsory||A weighted aggregate mark of 50% is required to pass the module|
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 2021/2 academic year.