Study of advanced topics of current interest in the field of deep learning, with an emphasis on understanding the network architecture of the pre-trained large foundation models. Selected topics include large langauge models, large vision models, and large audio models.

In this course, students are expected to explore some mathematical foundations of modern machine learning under a problem-solving framework. Topics include probability theory, frequentist statistics, Bayesian statistics, tensor algebra, vector calculus, convex and stochastic optimization, sequential optimization, and dynamic programming. This class strongly emphasizes on developing problem-solving skills.

By completing this capstone project the students will get an opportunity to apply the knowledge and skills in data science to some real industrial projects. In particular, it will test their skills in data acquisition, data visualization, data wrangling, data organization, data modeling and data analysis. Unlike the other courses in the Data Science series, in this course, the students are expected to explore some projects that are more real and open-ended.