Module Information
Module Identifier
CSM6120
Module Title
INTRODUCTION TO INTELLIGENT SYSTEMS
Academic Year
2009/2010
Co-ordinator
Semester
Semester 1
Other Staff
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Presentation and discussion of analytic report on scientific paper(s) | 20% |
| Semester Assessment | Essay - topic in Intelligent Systems (3000 words). | 80% |
| Supplementary Assessment | Will take a form as agreed by Departmental exam board | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
Describe and use the basic principles of Artificial Intelligence and Machine Learning
Be able to reflect on project needs
Practically apply AI and ML principles to meet those needs
Present the material they have learned in an informed, clear manner
Demonstrate understanding and insight into the material that they are presenting
Brief description
This module introduces the key ideas in Artificial Intelligence and ensures all students are at roughly the same level before moving on to the specialist modules.
Content
1. Introduction (3 hrs)
General introduction to Artificial Intelligence (AI), including discussion of what AI is, its history, definitions, and philosophical debates on the issue (the Turing test and the Chinese room). Ethical issues.
2. Search (6 hrs)
Why search is important in AI and how to go about it. This includes both informed and uninformed strategies. Evolutionary search.
3. Knowledge Representation (4 hrs)
Ways of representing knowledge in a computer-understandable way. Semantic networks, rules. Examples of the importance of KR.
4. Neural networks and subsymbolic learning (5hrs)
We can find solutions using search, but how can we remember solutions, learn from them and adapt them to new situations? This will cover perceptrons, single-layer and multi-layer networks.
5. Propositional and First-Order Logic (4 hrs)
The backbone of knowledge representation.
6. Programming for Intelligent Systems (3 hrs)
Practical introduction to programming for Intelligent Systems, used to illustrate search, KR and first-order logic.
7. Rule-based systems (3 hrs)
How can human expertise be automated? How to build an expert system - system concepts and architectures. Rule-based systems: design, operation, reasoning, backward and forward chaining.
8. Knowledge Acquisition and it importance in KR and RBS. (2 hrs)
General introduction to Artificial Intelligence (AI), including discussion of what AI is, its history, definitions, and philosophical debates on the issue (the Turing test and the Chinese room). Ethical issues.
2. Search (6 hrs)
Why search is important in AI and how to go about it. This includes both informed and uninformed strategies. Evolutionary search.
3. Knowledge Representation (4 hrs)
Ways of representing knowledge in a computer-understandable way. Semantic networks, rules. Examples of the importance of KR.
4. Neural networks and subsymbolic learning (5hrs)
We can find solutions using search, but how can we remember solutions, learn from them and adapt them to new situations? This will cover perceptrons, single-layer and multi-layer networks.
5. Propositional and First-Order Logic (4 hrs)
The backbone of knowledge representation.
6. Programming for Intelligent Systems (3 hrs)
Practical introduction to programming for Intelligent Systems, used to illustrate search, KR and first-order logic.
7. Rule-based systems (3 hrs)
How can human expertise be automated? How to build an expert system - system concepts and architectures. Rule-based systems: design, operation, reasoning, backward and forward chaining.
8. Knowledge Acquisition and it importance in KR and RBS. (2 hrs)
Notes
This module is at CQFW Level 7