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Advances in machine learning (ML) and deep learning are constantly transforming prototypes in research labs to valid solutions to real-world problems. Using ML entails developing end-to-end pipelines to collect data, preprocess it, and run learning and inference algorithms in a scalable manner. This results in computationally intensive workloads and complex software pipelines. Systems for ML help users organize their data and scale these computationally intensive problems to larger and larger datasets. This seminar will review cutting-edge research on these topics.

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This course builds on building technology fundamentals and focuses on the design, control, and optimization of environmental (heating, cooling, and plumbing) systems in buildings. Students will learn how to analyze, design, and optimize active heating and cooling technology, including plumbing systems, in both residential and commercial buildings. In the lab, students will gain expertise in building energy modeling (BEM) using a state-of-the-art BEM tool (eQuest) to optimize the energy performance of an existing campus building.

Through readings, presentations and group activities, students are introduced to the principles of human-centered design. The engineering design process is explored through assignments that guide students in ideation, testing and documentation of an engineering system. Students engage in hands-on workshops to learn and practice new technical skills, and they apply these tools towards completing a semester-long collaborative project to design, build and program an autonomous mobile robot. Enrollment limited to 20.