Teaching

强化学习

1. Assessment：

   • Homework: 30%
   • Course Project: 70%

2. References：

   • Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.

优化博弈与机器学习（全英文）

1. Assessment：

   • Class participation: 30%
   • Exercise: 70%

2. Related Topics：

   • Optimization theory and algorithms: gradient descent algorithms, primal-dual methods, accelerated methods, etc.
   • Game theory and algorithms: minimax optimization, Nash equilibrium seeking, etc.
   • Optimization in learning: stochastic gradient descent, variance reduced methods, etc.
   • Learning to optimize: leverages machine learning to develop optimization methods.

3. Some References：

   • S. Bubeck, Convex optimization: Algorithms and complexity, Foundations and Trends in Machine Learning, vol. 8, no. 3–4, pp. 231–357, 2015.
   • L. Bottou, F. E. Curtis, and J. Nocedal, Optimization methods for large-scale machine learning, SIAM Review, vol. 60, no. 2, pp. 223–311, 2018.
   • B. Franci, S. Grammatico, and M. Staudigl, Distributed generalized Nash equilibrium seeking: An operator-theoretic perspective, IEEE Control Systems, pp. 87–102, 2022.
   • Dimitri P. Bertsekas, Nonlinear Programming, 3rd edition, 2016.
   • T. Chen et al., Learning to optimize: A primer and a benchmark, Journal of Machine Learning Research, vol. 23, pp. 1–59, 2022.