Introduction to Artificial Intelligence
Duration
One Semester or equivalent
Contact hours
- 32 hours face to face + blended
On-campus unit delivery combines face-to-face and digital learning.
Prerequisites
or
Assumed Knowledge Object oriented programming at an intermediate level
Aims and objectives
This unit is designed to give students a broad outline of algorithmic problem solving and the basic concepts of artificial intelligence. It is assumed that students already have good programming skills in at least one of the programming languages Java/C/C++.
Unit Learning Outcomes (ULO)
On successful completion of this unit students will be able to:
1. Describe and interpret the fundamental concepts of Artificial Intelligence (AI) and generic problem solving techniques
2. Apply advanced algorithms and data structures to solve common problems
3. Design software that implements AI concepts.
Unit Learning Outcomes (ULO)
On successful completion of this unit students will be able to:
1. Describe and interpret the fundamental concepts of Artificial Intelligence (AI) and generic problem solving techniques
2. Apply advanced algorithms and data structures to solve common problems
3. Design software that implements AI concepts.
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
Live Online Lecture | 2 | 8 | 16 |
| On Campus Class | 2 | 4 | 8 |
| On Campus Class | 1 | 12 | 12 |
| Online Activities | 2 | 12 | 24 |
| Unspecified Activities Independent Learning | 8.5 | 12 | 102 |
TOTAL | 150 hours |
Swinburne Online
Type | Hours per week | Number of Weeks | Total |
| Online Activities | 12.5 | 12 | 150 |
TOTAL | 150 hours |
Assessment
Types | Individual/Group task | Weighting | Unit Learning Outcomes (ULOs) |
Assignment 1 | Individual | 20-30% | 2,3 |
Assignment 2 | Group | 20-30% | 2,3 |
Final Semester test | Individual | 20-30% | 1,2 |
| Mid Semester test | Individual | 20-30% | 1,2 |
Content
• Introduction to Artificial Intelligence and Intelligent Agents
• Introduction to Logic and Reasoning
• Uninformed and Informed Search
• Knowledge Representation
• Expert Systems
• AI Planning
• Uncertain Knowledge and Reasoning
• Decision Making with Uncertainty
• Adaptation and Machine Learning
• Philosophical Aspects of AI
• Introduction to Logic and Reasoning
• Uninformed and Informed Search
• Knowledge Representation
• Expert Systems
• AI Planning
• Uncertain Knowledge and Reasoning
• Decision Making with Uncertainty
• Adaptation and Machine Learning
• Philosophical Aspects of AI
Study resources
- Reading materials.
Reading materials
A list of reading materials and/or required texts will be made available in the Unit Outline.