ARTIFICIAL INTELLIGENCE FUNDAMENTALS

CSE5AIF

2020

Credit points: 15

Subject outline

Artificial Intelligence (AI) is the field of engaging computers for reasoning and decision-making. In this subject, you will be introduced to fundamental concepts and different research fields of AI. Main topics include state space search, game-playing, knowledge representation, theorem-proving, rule-based systems, machine learning and artificial agents. Python will be used as the implementation programming language.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorNasser Sabar

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE4IP

Co-requisitesN/A

Incompatible subjects CSE2AI, CSE2AIF

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsArtificial intelligence: structures and strategies for complex problem solving.PrescribedLuger, G. 20096TH EDN., ADDISON WESLEY
ReadingsArtificial intelligence: A modern approach.RecommendedRussel, S. and Norvig, P. 20163RD EDN., PRENTICE- HALL

Graduate capabilities & intended learning outcomes

01. Devise appropriate representations for state space search, and other search problems including two-player games.

Activities:
This topic is introduced in lectures and students explore representation schemes on simple problems in practice classes.

02. Represent knowledge using predicate calculus, and manually apply resolution refutation theorem-proving.

Activities:
This topic is introduced in lectures and practice classes give students direct experience in applying these techniques to reasoning problems.

03. Represent simple situations using a variety of other discipline- specific knowledge representation schemes including conceptual graphs and frames.

Activities:
This topic is introduced in lectures and practice classes allow students to represent situations using both frame representations and conceptual graph representation schemes

04. Construct simple rule-based systems using an expert system shell.

Activities:
Theory of Expert Systems is covered in lectures. In practical classes, students construct simple expert systems using the Python language.

Subject options

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Start date between: and    Key dates

Melbourne, 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorNasser Sabar

Class requirements

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Computer Laboratory Week: 11 - 22
One 2.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
One 3,000-word assignment50 01, 02, 04
One 2-hour examination (equivalent to 2000 words)50 01, 02, 03, 04

Melbourne, 2020, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorNasser Sabar

Class requirements

Lecture Week: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Computer Laboratory Week: 32 - 43
One 2.0 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
One 3,000-word assignment50 01, 02, 04
One 2-hour examination (equivalent to 2000 words)50 01, 02, 03, 04