3.0

credits

Average Course Rating

This course aims at helping students learn about how machine learning techniques are increasingly embraced by the field of finance. We will explore: (a) various topics and problems in finance that have been/will be benefited from the advances in machine learning, including but may not limited to portfolio optimization, asset pricing, market microstructure, high frequency trading, et cetera.; (b) different models of deep learning, (inverse) reinforcement learning and transfer learning that have been applied to tackle financial problems or have great potential in doing so. Recent advances, such as market prediction via special designs of neural networks, market simulator using generative adversarial networks, trading with reinforcement learning, portfolio optimization assisted by transfer learning and so on, will be discussed in this course. While this course is not intended to be highly theoretical, some familiarity with real analysis, optimization, probability and stochastic processes (Brownian motion, Markov processes, Poisson processes, martingales), and machine-learning would be helpful.