Techniques for decision making under uncertainty and overview of necessary tools for building autonomous and decision-support systems; computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information; Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes (POMDPs).
June 2020 August 2020
Two-week course for Stanford Pre-Collegiate Studies (SPCS) Summer Institutes (SI) and International Institutes (II). Overview of modern artificial intelligence; development of mathematical and programming proficiency in machine learning and optimization, including supervised learning, unsupervised learning, and reinforcement learning algorithms.
Spring 2018 Fall 2018 Spring 2019
Numerical and analytical simulation of physical problems in sciences and engineering using applied methods; developing and using numerical techniques for physical problems described by nonlinear algebraic equations, ordinary and partial differential equations.
Differentiation and integration techniques and their applications (area, volumes, work), improper integrals, approximate integration, analytic geometry, vectors, infinite series, power series, Taylor series, computer algebra.
Study of functions, graphs of polynomial and rational functions, radical functions, exponential and logarithmic functions, inequalities, trigonometric functions, fundamental identities, right triangles, trigonometric equations.