Computer Science

 

ECM2411 - Machine Learning and Artificial Intelligence **NOT RUNNING IN 2012/13** (2012)

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MODULE TITLEMachine Learning and Artificial Intelligence **NOT RUNNING IN 2012/13** CREDIT VALUE15
MODULE CODEECM2411 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12 0 0
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

Artificial Intelligence is the science of getting computers to do things which, when done by humans, involve the exercise of intelligence. It has been an important strand of Computer Science throughout the lifetime of that discipline, and has exerted a significant influence on other areas of Computer Science as well as on practical applications. Machine Learning is an important sub-area of Artificial Intelligence, in which mechanisms are developed which enable machines to learn from experience just as humans do; it is a key focus of Computer Science research at Exeter   This module will provide you with a broad overview of both Artificial Intelligence in general and Machine Learning in particular, as well as a more detailed understanding, both practical and theoretical, of selected topics within these areas. This module is suitable for any student who has a basic knowledge of computer programming, as well as linear algebra, discrete mathematics, and probability theory.

AIMS - intentions of the module

This module is divided into three sections. The first section provides a general introduction to Artificial Intelligence and Machine Learning, including reference to topics which will be handled in greater depth in final-year options. The second section provides some useful technical tools for Artificial Intelligence, in the form of Formal Logic used as a representation and reasoning formalism and its practical implementation in the Prolog programming language. The third section provides a more detailed treatment of selected topics in AI and Machine Learning, reflecting the research interests of the lecturers.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Explain the nature of Artificial Intelligence, its scope, and its limitations
2. Use at least one AI programming language and understand its underlying theory
3. Use a variety of Machine Learning tools and techniques

Discipline Specific Skills and Knowledge

4. Describe a number of different programming paradigms
5. Learn new computing techniques and understand how to apply them to real problems

Personal and Key Transferable / Employment Skills and Knowledge

6. Plan and execute an essay on a technical topic
7. Adapt existing technical knowledge to learning new methods

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Part I (5 lectures): General introduction to AI and Machine Learning, including reference to such topics as connectionism, pattern recognition, and evolutionary computing.

Part II (5 lectures): An introduction to Formal Logic as a representation and reasoning formalism for AI, and its practical implementation in Prolog.

Part III (10 lectures): A more detailed treatment of a range of AI topics such as machine learning, game playing, natural language processing, and the philosophy of AI (the exact selection of topics may vary from year to year),

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 35.00 Guided Independent Study 115.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching 20 Lectures
Scheduled Learning & Teaching 15 Workshops and tutorials
Guided Independent Study 115 Coursework, private study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
       
       
       
       
       
SUMMATIVE ASSESSMENT (% of credit)
Coursework 60 Written Exams 40 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam 40 90 minutes 1, 2, 3, 4, 5, Oral, on request
Technical exercise 30 15 hours 2, 3, 5, 7 Individual Marksheet
Essay 30 1500 words 1, 4, 6 Individual Marksheet
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-reassessment
All Examination 1, 2, 3, 4, 5 August
       
       

 

RE-ASSESSMENT NOTES

All reassessment will be by examination.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE – http://vle.exeter.ac.uk

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Russell S and Norvig P Artificial Intelligence: A Modern Approach 3rd Edition Pearson 2010 [Library]
Set Bratko I Prolog Programming for Artificial Intelligence 4th Edition Addison-Wesley 2011 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1701, ECM1707, ECM1408
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 2 (NQF level 5) AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 12 March 2012 LAST REVISION DATE Thursday 28 June 2012
KEY WORDS SEARCH Artificial Intelligence, Machine Learning, Logic Programming, Prolog