Blockchain, the underlying mechanism behind the bitcoin and other cryptocurrencies, is a revolutionary decentralized technology that is envisioned to be as impactful as internet. This course will introduce the technical foundations of blockchain and its applications to a wide range of industries including finance, computer science, supply-chain, smart power grid and social networking. The objective of this course is to provide students with required knowledge to conduct research on blockchain and basic skills to design smart contracts and implement distributed applications (DAPP).
Reinforcement learning (RL) is a computation approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. This course will introduce the mathematical theory of reinforcement learning and its applications to the design of human-level artificial intelligence. The objective of this course is to provide students with required knowledge to conduct research in reinforcement learning and basic skills to apply reinforcement learning techniques to novel real-world applications.
Convex optimization, a special class of mathematical optimizations, has been studied and widely used in engineering for decades. There are tremendous practical advantages for recognizing a problem as a convex optimization problem because nowadays the convex problem can be solved reliably and efficiently by using off-the-shelf software tools. On the other hand, there are great theoretical advantages for casting a problem into a convex form, which often has an interesting interpretation in terms of the original non-convex problem. This course aims to provide students with tools and training to formulate and solve convex problems in advanced research and in broad engineering areas, such as machine learning, financial engineering, computer vision, signal processing, circuit design, control and communications.