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Self-Learning Systems

 

Sep 25 2023, Monday - Sep 28 2023, ThursdaySee Schedule below for times (GMT +8:00) Kuala Lumpur, Singapore

 

See below for location details

, Singapore

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Overview

Developing self-learning systems requires the use of various techniques covering vast areas of machine learning, evolutionary computation, etc. It is important for IT professionals especially AI engineers to acquire the cutting-edge knowledge and skills in this area in order to develop self-learning systems.
This course is part of Graduate Certificate in Intelligent Software Agents series.

Course Description & Learning Outcomes

This course presents the core theory and algorithms of reinforcement learning and evolutionary learning techniques, and practical skills and strategies for real-world industrial implementations. At the end of the course, 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

Pre-course instructions

- 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. Registration close date: 04/09/2023

Schedule

Date: 25 Sep 2023, Monday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615

Date: 26 Sep 2023, Tuesday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615

Date: 27 Sep 2023, Wednesday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615

Date: 28 Sep 2023, Thursday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615

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