Bayesian Statistical Inference and Machine Learning II
Instructor: Gordon Ritter
This course helps to prepare students for careers in finance, financial engineering, and financial data science, both on the sell and the buy sides. The course teaches cutting-edge tools and methods that drive investment decisions at quantitative trading firms, and, more generally, firms applying machine learning to data science. The course will combine presentations of theory, immediately followed by in-class programming examples using real financial data. Students will subsequently build upon these examples in their homework and projects.
This is the second half of a one-quarter course that has been split into two half-quarter units. It can be taken without the first part by students who are especially motivated, or who have had prior experience in machine learning. In the first two lectures, we will focus on support vector machines, gaussian processes, neural networks, and kernel-based methods. The final two lectures will focus on the new, but rapidly-growing area of Reinforcement Learning for finance. There will be four lectures, followed by a final project that will use the methods discussed in class on real financial data.