Comprehensive course analysis
Who should attend
- 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
About the course
Self-Learning Systems are software injected with machine learning techniques which explores how to enable computers to learn from and make decisions based on data without explicit programming instructions. The basic building block of self-learning systems is the ability for a system to learn based on experience, make inferences from disparate signals, and then take action in response to new or unforeseen events. Self-Learning Systems have to be able to evolve, self-develop, self-learn continuously in order to adapt to the dynamically changing environment.
Developing self-learning systems requires the use of various techniques covering vast areas of machine learning, evolutionary computation, image processing, audio/video processing, 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 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, 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
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
Dr. Zhu Fangming is with the Institute of Systems Science of the National University of Singapore (NUS-ISS). He currently lectures in the Master of Technology programme in the areas of evolutionary computation, neural networks and data mining. Prior to joining ISS, he was a postdoctoral fellow i...
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