Current Course at UCLA


  • CS 269: Foundations of Deep Learning, Winter 2019

  • Deep learning has achieved great success in many applications such as image processing, speech recognition and Go games. However, the reason why deep learning is so powerful remains elusive. The goal of this course is to understand the successes of deep learning by studying and building the theoretical foundations of deep learning. Topics covered in this course include but are not limited to: expressive power of deep learning, optimization for deep learning, generalization performance of deep learning and robustness of deep learning. Instructor will give lectures on advanced topics of statistical learning theory. Students will present and discuss papers on the selected topics, and do a course project.

  • CS 260: Machine Learning, Fall 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 at UVa