CS-456: Deep reinforcement learningThis course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approac
PHYS-442: Modeling and design of experimentsIn the academic or industrial world, to optimize a system, it is necessary to establish strategies for the experimental approach. The DOE allows you to choose the best set of measurement points to min
EE-320: Analog IC designIntroduction to the design of analog CMOS integrated circuits at the transistor level. Understanding and design of basic structures.
MATH-261: Discrete optimizationThis course is an introduction to linear and discrete optimization.
Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to p
ENV-525: Physics and hydrology of snowThis course covers principles of snow physics, snow hydrology, snow-atmosphere interaction, and snow modeling. It transmits detailed understanding of physical processes within the snow and at its inte
ENG-466: Distributed intelligent systemsThe goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture
ENG-272: Fluid mechanics (for SIE)This course helps students acquire basic knowledge of the main concepts and equations of fluid mechanics and develop the skills necessary to work effectively in professional engineering practice.