Quanquan Gu

Assistant Professor

Current Course


  • CS 6316/ SYS 6016: Machine Learning, Spring 2018

  • This course introduces the foundational theory and algorithms of machine learning. The goal of this course is to endow the student with a) a solid understanding of the foundational concepts of machine learning, and b) the ability to derive and analyze machine learning algorithms. Topics to be covered include online learning, empirical risk minimization, PAC learning, Agnostic PAC learning, boosting, structural risk minimization, decision trees, surrogate loss functions, stochastic gradient descent, support vector machines, kernel methods, multi-class classification, neural networks, dimensionality reduction, and clustering, etc.

Past Courses