Australia Artificial Intelligence Institute – University of Technology Sydney, Australia
Brain Computer Interface Brain-computer interface (BCI) is to enhance human brain’s capability to interact with the environment through a direct pathway. Nowadays, BCI is widely considered as a disruptive technology for the next-generation technology which brings the incredible potential for various fields such as augmenting human performance, health care and computational neuroscience. As Artificial Intelligence and Augmented Reality technology even bring significant benefits to boost the development of BCI. This tutorial intends to bring up the background of BCI, various designs of BCI, current BCI challenges and further BCI applications to different audiences from various research fields. Then the multidisciplinary researchers can bring new insights to bridge the BCI to human real life.
Department of Electrical Engineering, National Tsing Hua University
Reinforcement Learning: Basics, Applications, Challenges, and Opportunities Machine learning can be classified into three major branches: supervised, unsupervised, and reinforcement learning. Over the past few years, supervised and unsupervised learning have been applied to various engineering problems and shown promising results. Most recently, reinforcement learning has emerged as a powerful tool addressing dynamical systems, attracting much attention in both academia and industry. This tutorial intends to bring up the basic concepts of reinforcement learning, associated applications, and challenges and future directions. Background of reinforcement learning is given first; basic building blocks of reinforcement learning such as Markov decision process, Bellman equations, value iteration and policy iteration are introduced. Value-based, policy based, and model-based methods are then discussed. We move on to multi-agent reinforcement learning, which can address a variety of complex problems. Several applications are examined, including autonomous systems, supply chain management, smart energy systems, and smart city. We will finish with some discussions on the challenges of real-world reinforcement learning and possible research directions.

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