Introduction to Expert Systems
- Sciences
- 400 level
- 2 credit units
- 80 pages
- 11 units
This course introduces the fundamental concepts of expert systems, a branch of artificial intelligence. It covers the components, development, and applications of expert systems across various domains. Students will learn about knowledge representation techniques, including rule-based, frame-based, and fuzzy logic systems. The course also explores neural network-based expert systems and their applications, equipping students with the skills to design and implement intelligent systems.
About this course
- Difficulty
- Intermediate
- Study hours
- 40 hours
- Maths
- Basic
- Content
- Theoretical, case study
- Practical work
- No
- Basic programming skills
- Familiarity with data structures
- Introduction to Artificial Intelligence
- Assignments
- Tutor marked assignments
- Final examination
What you'll read
The real module and unit structure of CIT474, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
CIT474 · Unit 1: Introduction to Expert Systems
Expert systems (ES) are systems that emanate from the new area of computing known as Artificial Intelligence (AI). AI is the branch of Computer Science concerned with developing computers that behaves like humans. Precisely, Expert systems occupy a central place in the cognitive science aspect of artificial intelligence as shown in figure 1 below.
What you should be able to do
- Understand the basic concepts of expert systems.
- Identify the components of an expert system and their functions.
- Apply different knowledge representation techniques.
- Design and develop rule-based expert systems.
- Explain the principles of fuzzy logic and neural networks in expert systems.
- Evaluate and select appropriate expert system tools for specific applications.
What it prepares you for
- AI Developer
- Knowledge Engineer
- System Analyst
- Data Scientist
- Software Engineer
- Healthcare
- Finance
- Manufacturing
- Aerospace
- Telecommunications
- CLIPS
- JESS
- Drools
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 1: Basic Concept of Expert Systems
Unit 4: Knowledge Representation in expert systems
Understanding the nuances of knowledge representation requires grasping abstract concepts and their practical implications.
- Module 2: Classes of Expert System
Unit 3: Fuzzy and neural network based expert system
Requires understanding of mathematical concepts and their application in reasoning under uncertainty.
A suggested way through it
13 weeks, about 28 hours in total. Yours will differ.
- Week 1Module 1: Basic Concept of Expert Systems
Unit 1: Introduction to Expert Systems · 2 hours
Understand the historical background of expert systems.. Define an expert system and its key features.. Identify the roles of individuals involved in expert system development.. List the advantages and disadvantages of expert systems..
- Week 2Module 1: Basic Concept of Expert Systems
Unit 2: Components of Expert Systems, and Development of an Expert System · 2 hours
Discuss the components of an expert system: user interface, inference engine, knowledge base, working memory, and explanation facility.. Understand how expert systems operate.. Explain the steps involved in developing an expert system..
- Week 3Module 1: Basic Concept of Expert Systems
Unit 3: The Need for Expert Systems and Applications · 2 hours
Discuss the need for expert systems in various organizations.. Identify factors that make an expert system appropriate for a given problem.. Explore the application areas of expert systems, including accounting, finance, agriculture, and medicine..
- Week 4Module 1: Basic Concept of Expert Systems
Unit 4: Knowledge Representation in Expert Systems · 2 hours
Discuss knowledge representation in expert systems.. Explain different types of knowledge representation methods, such as production rules, semantic nets, and frames.. Analyze the benefits and disadvantages of each knowledge representation method..
- Week 5Module 2: Classes of Expert System
Unit 1: A rule-based expert system · 2 hours
Explain rule-based systems and their characteristics.. Study the example of Mycin, a rule-based expert system.. Analyze the components of Mycin and its history.. Discuss the advantages and disadvantages of rule-based systems..
- Week 6Module 2: Classes of Expert System
Unit 2: Frame-based expert system · 2 hours
Understand the concept of frame-based expert systems.. Define terms associated with frame-based systems, such as frames, slots, and attributes.. Explain how demons are triggered in frame-based systems..
- Week 7Module 2: Classes of Expert System
Unit 3: Fuzzy and neural network based expert system · 2 hours
Understand fuzzy logic-based expert systems.. Understand neural network-based expert systems.. Discuss the advantages and disadvantages of each type of system..
- Week 8Module 2: Classes of Expert System
Unit 4: Blackboard Expert System – HEARSAY · 2 hours
Define a blackboard system and its components.. Identify the components of the Hearsay expert system.. Discuss the benefits of blackboard architecture..
- Week 9Module 2: Classes of Expert System
Unit 5: Expert System Shells · 2 hours
Understand how to select an expert system for an organization.. Know the criteria for selecting an expert system.. Discuss the factors to consider when choosing an expert system-based tool..
- Week 10Module 2: Classes of Expert System
Unit 5: Expert System Shells · 2 hours
Define expert system shells.. Explain the components of a shell, including the knowledge base, reasoning engine, knowledge acquisition subsystem, explanation subsystem, and user interface..
- Week 11Module 3: Current trends in expert systems.
Unit 1: New development in expert systems · 2 hours
Discuss the current trends in expert systems.. Understand the industries with wide expert system applications.. Explore new developments in expert systems, such as machine learning and data mining..
- Week 12Module 3: Current trends in expert systems.
Final Revision · 3 hours
Review all modules and units.. Focus on key concepts and definitions.. Practice problem-solving techniques..
- Week 13Module 3: Current trends in expert systems.
Final Revision · 3 hours
Complete assignments and prepare for the final examination.. Review tutor-marked assignments and feedback.. Consolidate understanding of course materials..
Preparing for the exam
- Review all lecture notes and study materials thoroughly.
- Practice solving problems and case studies from the course.
- Focus on understanding the key concepts and definitions.
- Create concept maps linking different modules and units.
- Practice with expert system development tools like CLIPS or JESS.
- Review all tutor-marked assignments and feedback.
- Allocate time for revision and practice questions in the last two weeks.
Questions students ask about this course
What is CIT474 about?
This course introduces the fundamental concepts of expert systems, a branch of artificial intelligence. It covers the components, development, and applications of expert systems across various domains. Students will learn about knowledge representation techniques, including rule-based, frame-based, and fuzzy logic systems. The course also explores neural network-based expert systems and their applications, equipping students with the skills to design and implement intelligent systems.
How many units does CIT474 have?
CIT474, Introduction to Expert Systems, has 11 units across 3 modules, over 80 pages of course material. You can read it one unit at a time.
How many credit units is CIT474?
CIT474 carries 2 credit units, at 400 level in Sciences.
Is CIT474 hard?
CIT474 is rated intermediate level, with basic mathematical content. It is mostly theoretical and case study work.
How long does CIT474 take to study?
About 40 hours of study, spread across its 11 units.
How is CIT474 assessed?
CIT474 is assessed by assignments, tutor marked assignments and final examination.
What do I need before starting CIT474?
Basic programming skills Familiarity with data structures Introduction to Artificial Intelligence
What can I do with CIT474?
AI Developer, Knowledge Engineer, System Analyst, Data Scientist and Software Engineer.