The BCS Foundation Award in Knowledge-Based Systems has been designed for individuals interested in furthering their understanding of the more technical aspects of AI, and how knowledge-based intelligence can be used to compliment more modern AI techniques such as Machine Learning.
Knowledge-Based Systems are designed to capture human expertise in order to enable AI applications to make intelligent decisions.
Through completion of this award candidates will have the ability to recognise Knowledge-Based Systems, gain an understanding of how they work, and consider how they can add value to an organisation.
This award will also enable candidates to understand the concept of Uncertainty and Fuzzy Logic, and how Knowledge-Based Systems can be used to help organisations to make decisions and act where there is higher level of uncertainty.
This award has been created alongside a selection of other awards in the AI space which offer candidates a clear pathway of progression into other disciplines of IT along with a broader knowledge of AI in the workplace. This makes it ideally suited for those looking for a change in career, an upskilling workforce, sustainable employers and individuals with a background in: science, engineering, knowledge engineering, finance, education or IT services.
You'll learn about:
This award contributes 5 credits towards the:
Learning Objectives
How a Knowledge-Based System works and its role within an AI solution 1/2
How a Knowledge-Based System Works and its Role Within an AI Solution 2/2
A Brief Summary of the Third and Fourth Industrial Revolution
The Difference Between Knowledge-Based Intelligence and Computational Intelligence
How a Knowledge-Based System complements Machine Learning - Case Based Reasoning and Symbolic Learning
Stages of case-based reasoning Case Based Reasoning
How a Knowledge-Based System Complements Machine Learning - Expert Systems
How a Knowledge-Based System Complements Machine Learning - Rule Based Systems
How a Knowledge-Based System Complements Machine Learning - Hypertext Manipulation and Intelligent Tutoring Systems
Neural Networks and Self-Driving Cars
Learning Objectives - recap
Knowledge check
Learning Objectives
The Use of Rules Within a Knowledge-Based System
Examples of Statements Used Within Rules
The Difference Between Forward Chaining and Backward Chaining 1/2
Forward Chaining Example
The Difference Between Forward Chaining and Backward Chaining 2/2
Backward Chaining Example
Learning Objectives - recap
Knowledge check
Learning Objectives
The Concept of Uncertainty in Knowledge-Based Systems - Bayes’ Theorem
The Concept of Uncertainty in Knowledge-Based Systems - Fuzzy Logic 1/2
The Concept of Uncertainty in Knowledge-Based Systems - Fuzzy Logic 2/2
Fuzzy Logic and the Degree of Truth
Learning Objectives - recap
Knowledge check
If you change your mind about your course purchase, you can apply for a refund within 14 days of purchase, provided that you haven't accessed your course material. Please note that you are no longer eligible if you have accessed your course material or communicated with any course tutor.
