All Courses
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2.096/6.336/16.910 Intro to Modeling & Simulation
Introduction to computational techniques for modeling and simulation of a variety of large and complex engineering, science, and socio-economical systems. Prepares students for practical use and development of computational engineering in their own research and future work. Topics include mathematical formulations (e.g., automatic assembly of constitutive and conservation principles); linear system solvers (sparse and iterative); nonlinear solvers (Newton and homotopy); ordinary, time-periodic and partial differential equation solvers; and model order reduction. Students develop their own models and simulators for self-proposed applications, with an emphasis on creativity, teamwork, and communication. Prior basic linear algebra and programming (e.g., MATLAB, Julia or Python) helpful.
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6.881 Tissue vs Silicon in Machine Learning
In this course we will examine how biological neural circuits and brain function can affect the design of machine learning hardware and software, and vice versa. We will attempt to build a better understanding of how similar and different the computational approaches of the two are, and what can be deduced from one area about the other. Examples will be biological constructs such as biological neurons, cortical columns, connectomes, associative memory, and natural processes like pruning, versus artificial neural network hardware and software designs. We will look at plausible alternative learning mechanisms to backpropagation such as feedback alignment, equilibrium propagation and predictive coding, and try to better understand state of the art machine learning optimization techniques such as sparsification and quantization. The class will be in a seminar style, based on a select collection of papers on the topic.