CS3033: ARTIFICIAL INTELLIGENCE (2021-2022)

Last modified: 31 May 2022 13:05

Course Overview

The course provides an introduction to Artificial Intelligence (AI). It discusses fundamental problems of AI and their computational solution via key concepts.

Course Details

Study Type	Undergraduate	Level	3
Term	First Term	Credit Points	15 credits (7.5 ECTS credits)
Campus	Aberdeen	Sustained Study	No
Co-ordinators	Dr Mingjun Zhong Dr Shouyong Jiang

What courses & programmes must have been taken before this course?

Any Undergraduate Programme
One of CS2007 Algorithmic Problem Solving (Passed) or CS2011 Algorithmic Problem Solving (Passed) or CS2012 Advanced Web Application Development (Passed) or CS2509 Advanced Web Application Development (Passed) or CS2512 Advanced Web Application Development (Passed) or CS2521 Algorithmic Problem Solving (Passed) or CS2522 Algorithms and Data Structures (Passed) or KL258D Advanced Web Application Development (Passed) or Bachelor Of Laws With Computing Science (With Honours)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

CS3025 Knowledge-Based Systems (Studied)

Are there a limited number of places available?

Course Description

The course provides an introduction to Artificial Intelligence (AI). It discusses fundamental problems of AI and their computational solution via key concepts.

Content: Introduction and Agents; Uninformed search. Search; Informed search; Adversarial Search; Constraint Satisfaction Problems; Logical Agents, propositional reasoning; Probabilistic Reasoning under Uncertainty; Planning; Symbolic Machine Learning; Neural Nets.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers

Details, including assessments, may be subject to change until 30 August 2024 for 1st term courses and 20 December 2024 for 2nd term courses.

Summative Assessments

Alternative Assessment

Critical essay and problem solving exercise 50%

Exam 50%

Alternative Resit Assessment

Where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).

Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Evaluate	Ability to analyze and evaluate proposed AI solutions in their context
Conceptual	Understand	Understanding of major issues, problems, solutions, and concepts in AI
Reflection	Create	Ability to apply AI concepts, techniques, and tools to creatively solve problems