Overview
Artificial intelligence (AI) exists in modern computer systems, most times behind the scene. This course will reveal those intelligent techniques leading to reasoning ability and smart behaviours embedded in intelligent software systems, which make use of knowledge (digitized data useful to business), learn, reason and take actions automatically, in various business contexts and industry domains.
Course Description & Learning Outcomes
This course is a part of the Artificial Intelligence and Graduate Certificate in Intelligent Reasoning Systems, which is a part of the Stackable Graduate Certificate Programme in Artificial Intelligent Systems offered by NUS-ISS. 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. Upon completion of the course, participants will be able to: - Identify needs of machine reasoning technology in various industrial applications, for decision automation. - Acquire knowledge of core machine reasoning techniques, including rule/process-based logical reasoning, domain expert knowledge acquisition and representation, knowledge discovery, and handling uncertainty during reasoning process. - Apply data mining / machine learning techniques to extract knowledge from data, then express business rules/processes in computer readable format. - Create software application by applying learnt machine reasoning techniques and computer programming.
Recommended Prerequisites
This is an intensive, intermediate course. - 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.
Pre-course instructions
No printed copies of course materials are issued. Participants must bring their internet-enabled laptops with power charger to access and download course materials. Registration close date: 06/11/2023
Schedule
Date: 27 Nov 2023, Monday
Time: 12:00 AM - 12:00 AM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Agenda
| Day/Time | Agenda Activity/Description |
|---|---|
| Day 3 | - Artificial Intelligence: Technical Machine Inference - Knowledge Discovery by Data Mining / Machine Learning - Knowledge Discovery Workshop |
| Day 4 | - Contemporary Reasoning Systems - Creating Machine Reasoning System Workshop - Course Assessment |
| Day 1 | - Machine Reasoning Overview - Reasoning Types & System Architectures - Machine Reasoning Foundation Workshop |
| Day 2 | - Knowledge Acquisition & Representation - Knowledge Models (from the acquired to the represented) - Knowledge Modelling Workshop |
Pricing
Partners
