Key Takeaways
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.
Who Should Attend
This course is suitable for information technology professionals who are interested in creating intelligent computer software system able to make use of knowledge (digitised data useful to business), reason and take actions automatically, in various business contexts and industry domains.
This course will be useful for:
Artificial Intelligence Engineer who need develop competency in knowledge modelling, representation, discovery, knowledge graph, knowledge/rule base, and machine inference.
Software Developer/Engineer who need develop competency in business rule management system (BRMS) and business process management system (BPMS)
Application Solution Architect who need design intelligent system solutions and integrate them into enterprise system architecture
Data Scientist/Engineer who need obtain domain knowledge in artificial intelligence to assist data analytics.
Working professionals who need to upgrade existing machine reasoning knowledge and skills by practicing contemporary system building tool sets.
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.
What to Bring
No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.
If you are bringing a laptop, please see below for the tech specs:
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|
Minimum
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Recommended
|
|
Computer and processor
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1.6 GHz or faster, 2-core Intel Core i3 or equivalent, e.g. Apple (Intel) year 2012 model and newer
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Intel Core i7 or equivalent, e.g. Apple (Intel/M1/M2 chip) new models
|
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Memory
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4 GB RAM |
16 GB RAM
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Hard Disk
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256 GB disk size
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1 TB disk size
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Display
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800 x 600 screen resolution
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1280 x 768 screen resolution
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Graphics
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Graphics hardware acceleration requires DirectX 9 or later, with WDDM 2.0 or higher for Windows 10 (or WDDM 1.3 or higher for Windows 10 Fall Creators Update).
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DirectX 10 graphics card for graphics hardware acceleration
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Others
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An internet connection – broadband wired or wireless
Speakers and a microphone – built-in or USB plug-in or wireless Bluetooth
A webcam or HD webcam - built-in or USB plug-in |
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What Will Be Covered
This course will cover:
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
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
Fees & Subsidies
SkillsFuture Singapore (SSG) Funding 2024
| |
Full Course Fees |
Singapore Citizens & PRs aged 21 years and above
(70% funding support) |
Singapore Citizens aged 40 years and above
(90% funding support) |
Enhanced Training Support for SMEs (ETSS)
(90% funding support)
|
| Full course fee |
S$3800 |
S$3800 |
S$3800 |
S$3800 |
| SSG Funding |
- |
S$2660 |
S$2660 |
S$2660 |
| Nett course fee |
S$3800 |
S$1140 |
S$1140 |
S$1140 |
| 9% GST on nett course fee |
S$342 |
S$102.60 |
S$102.60 |
S$102.60 |
| Additional Funding if eligible under various schemes |
- |
- |
S$760 |
S$760 |
| Total nett course fee payable, including GST |
S$4142 |
S$1242.60 |
S$482.60 |
S$482.60 |
Note:
1. SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
3. SME fees are applicable only to participants who are sponsored by small and medium enterprises.
4. SSG Funding is valid up to 30 June 2024.
Certificate
Certificate of Completion
Participants have to meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.