Reasoning Systems
About This Course
• Identify real world business use cases and applications of advanced intelligent reasoning systems.
• Integrate advanced technical enablers in reasoning systems, including uniformed search, heuristic search, constraint satisfaction, simulation assisted learning, optimization, planning, system integration, programming, and data mining for knowledge discovery.
• Decompose complex application scenarios into sub problems to be solved by assembling cooperative intelligent subsystems.
• Reflect the architectures and techniques used in contemporary reasoning systems such as DeepMind AlphaGo Zero, Amazon Alexa Skills.
• Design cooperative reasoning modules based on decomposed business outcomes.
• Create hybrid reasoning system by applying suitable techniques and computer programming for complex business problem solving.
What You'll Learn
Entry Requirements
Participants who attended Machine Reasoning course as a prerequisite would transit smoother into this course. Participants who have not attended Machine Reasoning course could self-assess these essential abilities:
Able to read software source code written in one or more programming languages, e.g. web application, class diagram, extensible markup language (XML)
Able to write or convert source code into Java, e.g. score calculation Java example: CloudBalancingEasyScoreCalculator.java
Able to write business rules, e.g. score calculation Drools rule example: cloudBalancingScoreRules.drl
Able to operate one or more prevalent integrated development environment (IDE), e.g. Eclipse, IntelliJ, etc.
Able to operate one or more prevalent data mining software, e.g. RStudio, Orange, Weka, MATLAB, SPSS, SAS, etc.
Able to calculate basic probability.
Aware of decision tree, RESTful API, systems development life cycle (SDLC), unified modeling language (UML), client-server software architectur