EE 277: Reinforcement Learning: Behaviors and Applications (MS&E 237)
Reinforcement learning addresses the design of agents that improve decisions while operating within complex and uncertain environments. This course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include environment models, planning, abstraction, prediction, credit assignment, exploration, and generalization. Motivating examples will be drawn from web services, control, finance, and communications. Prerequisites: programming (e.g.,
CS106B), probability (e.g., MS&E 121,
EE 178 or
CS 109), machine learning (e.g.,
EE 104/
CME 107, MS&E 226 or
CS 229).
Terms: Aut
| Units: 3
FINANCE 229: MSx: Finance
This course covers the foundations of finance with an emphasis on applications that are vital for corporate managers. We will consider many important financial decisions made by corporate managers, both within the firm and in their interactions with investors. Essential to most of these decisions are financial valuations, which will be an important emphasis of the course. Topics include criteria for making investment decisions, valuation of financial assets and liabilities, relationships between risk and return, and capital structure choice.
Terms: Aut
| Units: 3
Instructors:
Zwiebel, J. (PI)
MS&E 237: Reinforcement Learning: Behaviors and Applications (EE 277)
Reinforcement learning addresses the design of agents that improve decisions while operating within complex and uncertain environments. This course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include environment models, planning, abstraction, prediction, credit assignment, exploration, and generalization. Motivating examples will be drawn from web services, control, finance, and communications. Prerequisites: programming (e.g.,
CS106B), probability (e.g., MS&E 121,
EE 178 or
CS 109), machine learning (e.g.,
EE 104/
CME 107, MS&E 226 or
CS 229).
Terms: Aut
| Units: 3
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