Computer Science

ECM2423 - Artificial Intelligence and Applications (2019)

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MODULE TITLEArtificial Intelligence and Applications CREDIT VALUE15
MODULE CODEECM2423 MODULE CONVENER Federico Botta (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 12
Number of Students Taking Module (anticipated) 23
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. This module will provide you with a broad overview of Artificial Intelligence, as well as a more detailed understanding, both practical and theoretical, of selected topics within this area. 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.

Pre-requisites: ECM1415 and ECM2418

AIMS - intentions of the module

In this module we aim to provide you with a general introduction to some of the main topics within the broad field of Artificial Intelligence, beginning with an overview of the history and philosophy of AI, then proceeding to a more detailed examination of a range of specific sub-areas, including logic and knowledge representation, searching algorithms, machine learning, and natural language processing.

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 Display competence in a range of Artificial Intelligence tools and techniques;
3 Explain the theoretical basis for a range of Artificial Intelligence methods;
4 Make use of Artificial Intelligence methods in practical applications.
Discipline Specific Skills and Knowledge:
5 Describe a number of different programming paradigms;
6 Learn a variety of computing techniques and apply them to real problems.
Personal and Key Transferable / Employment Skills and Knowledge:
7 Plan and execute a technical report;
8 Adapt existing technical knowledge to learning new methods.

SYLLABUS PLAN - summary of the structure and academic content of the module
  • philosophy of AI: the Turing Test and the Chinese Room, arguments and counter-arguments. Topics from the history of AI: The Dartmouth Conference; early work in reasoning, natural language, and microworlds; the "AI winter"; distributed AI and connectionism; logicism and its detractors, "neats" vs "scruffies"; the fifth generation project; embodied AI and cognitive robotics;
  • logic and knowledge representation: recapitulation and further exploration of first-order logic and logic programming. Reasoning strategies. Use of first-order logic as a knowledge representation language;
  • searching for solutions: hill-climbing, local search and simplex search (search and optimisation); decision trees (searching databases); minimax algorithm (searching gamespaces); A* (searching maps etc.);
  • machine learning: introduction (overview, and successful applications); machine learning algorithms including supervised learning models (kNN, Decision trees, Multi-layer Perceptron) and unsupervised learning (k-means clustering); learning and generalisation;
  • natural language processing (NLP): introduction to NLP (overview, history and successful stories), N-Gram language model, bigram and trigram HMM part-of-speech tagging.
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 and teaching activities 20 Lectures
Scheduled learning and teaching activities 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 20 Written Exams 80 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 80 2 hours - Summer Exam Period 1, 2, 3, 5, 6 Orally, on request
Technical exercise & report 20 20 hours 2,3,4,5, 6, 7, 8 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-assessment
All Written exam 1, 2, 3, 4, 5, 6, 8 August Ref/Def period
       
       

 

RE-ASSESSMENT NOTES

All reassessment will be by examination. For referred candidates, the mark will be capped at 40%. Deferred candidates will be awarded the higher of the uncapped exam mark alone and the uncapped exam mark combined with the coursework mark in the ratio 70:30.

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]
Set Christopher Bishop Pattern Recognition and Machine Learning Springer 2007 978-0387310732 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1415, ECM2418
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 5 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 10 July 2018
KEY WORDS SEARCH Artificial intelligence; machine learning; logic programming; prolog; knowledge representation; searching algorithms; natural language processing