Self�learning Systems
About This 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.
What You'll Learn
�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
Entry Requirements
Please see course weblink for more information