Results for EE 237/

This course will be an introduction to solar photovoltaics. No prior photovoltaics knowledge is required. Class lectures will be supplemented by guest lectures from distinguished engineers, entrepreneurs and venture capitalists actively engaged in solar industry. Past guest speakers include Richard Swanson (CEO, SunPower), Benjamin Cook (Managing Partner at NextPower Capital) and Shahin Farshchi (Partner, Lux Capital). Topics Include: Economics of solar energy. Solar energy policy. Solar cell device physics: electrical and optical. Different generations of photovoltaic technology: crystalline silicon, thin film, multi-junction solar cells. Perovskite and silicon tandem cells. Advanced energy conversion concepts like photon up-conversion, quantum dot solar cells. Solar system issues including module assembly, inverters, micro-inverters and microgrid. No prior photovoltaics knowledge is required. Recommended: EE116, EE216 or equivalent.

The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. 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).