This course focuses on the applications of data analytic, machine learning and deep learning methods. We will start with a quick review of basic Bayesian models followed by tools and concepts from artificial intelligence. Students will learn how to use deep learning to analyze a variety of complex real world problems. Numerous empirical examples from finance, internet analytics, and sports are used to illustrate the material covered. Google’s development of deep neural networks and applications will be discussed in detail.
Emphasis will be placed on understanding concepts of Bayes, AI and Deep Learning. The three main topics covered are: (i) Bayesian methods including conditional probability,hierarchical models (ii) Artificial Intelligence including modern regression methods such as lasso and ridge regression. Dimensionality reduction techniques and sparsity are central to data analysis (ii) Deep Learning including Neural Nets, Architecture design,Stochastic Gradient Descent, speeding up convergence. Throughout business and internet applications including machine intelligence, reinforcement learning, image and speech recognition will be used to illustrate the wide range of applications.