Key Takeaways
At the end of the course, the participants will be able to:
- Identify the requirements for self-learning systems in various industrial applications
- Understand the fundamentals of reinforcement learning and evolutionary learning techniques
- Design and develop self-learning systems using reinforcement learning and evolutionary learning techniques
- Assess the system performance and suggest possible improvements
Who Should Attend
This course is for:
- IT professionals who need to use reinforcement learning and evolutionary learning techniques to develop self-learning intelligent systems.
- IT professionals who need to assess and compare reinforcement learning and evolutionary learning techniques.
- Domain specialists planning to undertake self-learning systems development projects.
Prerequisites
- Basic programming skills in Python.
- Foundational knowledge in machine learning is strongly recommended.
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
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Computer and processor
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Intel Core i5 or equivalent
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Intel Core i7 or equivalent
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Memory
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8 GB RAM
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16 GB RAM
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Hard Disk
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500 GB disk size
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Display
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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
Introduction to Self-Learning Systems
Reinforcement Learning Systems
Deep Reinforcement Learning Systems
Model-based Reinforcement Learning Systems
Evolving Intelligent Systems
Evolutionary Learning Systems Using Evolutionary Computation Techniques
Practical Case Studies and Workshops
Fees & Subsidies
Fees for 2024
| |
Full Fee |
Singaporeans & PRs
(self-sponsored) |
| Full course fee |
S$3600 |
S$3600 |
| ISS Subsidy |
- |
(S$360) |
| Nett course fee |
S$3600 |
S$3240 |
| 9% GST on nett course fee |
S$324 |
S$291.60 |
| Total nett course fee payable, including GST |
S$3924 |
S$3531.60 |
Note:
- All fees and subsidies are valid from January 2024, unless otherwise advised.
- 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.
- From 1st January 2024, the GST will be increased to 9%.
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.