Machine Reasoning
About This Course
Identify needs of machine reasoning technology in various industrial
applications.
Acquire knowledge of core machine reasoning techniques, including rule-based
logical reasoning, domain expert’s knowledge representation and acquisition,
knowledge discovery, and handling uncertainty during reasoning process
Apply machine learning technique to extract industrial domain knowledge and
express business rules in computer readable format.
Compare the architectures and main techniques used in contemporary
reasoning systems, e.g. Google AlphaGo, IBM Watson, and Amazon Alexa.
Design knowledge based machine reasoning software modules based on
expected business outcomes and industrial domain knowledge
Build software application by applying learnt machine reasoning techniques
and computer programming.
What You'll Learn
Participants learn comprehensive knowledge of artificial intelligence (AI) fundamentals, automated computer/machine reasoning methods, knowledge discovery & modelling, decision support technologies, and intuitive graphics-based programming skills to design and create intelligent machine reasoning systems to solve real-world problems.
Entry Requirements
Participants should have intermediate mathematics and statistics knowledge, e.g. calculating boolean algebra (logic), and probability.
Participants should have intermediate computer literacy and software engineering fundamentals, e.g. using Windows or Linux or MacOS, Microsoft Office or LibreOffice, VMware or VirtualBox, and aware of web application, and client-server software architecture.
Participants should have current or prior hands-on coding experience in one or more high-level computer programming languages, preferable in Java. Experiences with Python, R, or structured query language (SQL) would have added advantages.
Participants without programming experience should self-study basic Java or Python.