ARTIFICIAL INTELLIGENCE FUNDAMENTALS
CSE2AIF
2020
Credit points: 15
Subject outline
This subject covers the fundamental areas of Artificial Intelligence, focussing on knowledge representation and search. Main topics include historical foundations and applications areas; state-space search; game-playing; knowledge representation languages including predicate calculus, semantic networks, conceptual graphs and frames; rule-based expert systems; and an introduction to symbolic and connectionist machine learning.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Chris Taylor
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 2 - UG
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE1IOO
Co-requisites: N/A
Incompatible subjects: CSE4FAI
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: N/A
Minimum credit point requirement: N/A
Assumed knowledge: N/A
Learning resources
Artificial intelligence: A modern approach.
Resource Type: Book
Resource Requirement: Recommended
Author: Russel, S. and Norvig, P.
Year: N/A
Edition/Volume: 2ND EDN
Publisher: PRENTICE-HALL
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Artificial intelligence: structures and strategies for complex problem solving.
Resource Type: Book
Resource Requirement: Prescribed
Author: Luger, G.
Year: N/A
Edition/Volume: 6TH EDN
Publisher: ADDISON WESLEY
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: N/A
Other description: N/A
Source location: N/A
Career Ready
Career-focused: No
Work-based learning: No
Self sourced or Uni sourced: N/A
Entire subject or partial subject: N/A
Total hours/days required: N/A
Location of WBL activity (region): N/A
WBL addtional requirements: N/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Melbourne (Bundoora), 2020, Semester 2, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Chris Taylor
Class requirements
Computer LaboratoryWeek: 32 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
Two 1.00 hour lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment 1 (equiv to 500 words) | N/A | N/A | No | 10 | SILO2, SILO6 |
Assignment 2 (equiv to 1000 words) | N/A | N/A | No | 20 | SILO2, SILO3, SILO5, SILO6 |
One 3-hour examination | N/A | N/A | No | 70 | SILO1, SILO2, SILO3, SILO4, SILO5, SILO6 |
Dandenong (Chisholm Institute), 2020, Semester 2, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Chris Taylor
Class requirements
Computer LaboratoryWeek: 31 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
Two 1.00 hour lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment 1 (equiv to 500 words) | N/A | N/A | No | 10 | SILO2, SILO6 |
Assignment 2 (equiv to 1000 words) | N/A | N/A | No | 20 | SILO2, SILO3, SILO5, SILO6 |
One 3-hour examination | N/A | N/A | No | 70 | SILO1, SILO2, SILO3, SILO4, SILO5, SILO6 |