Johns Hopkins University 3 credits Mechanical Engineering
This course covers machine learning fundamentals (e.g., optimization, perceptron, and universal approximation), some popular and advanced machine learning techniques (e.g., Supervised, Unsupervised, Probabilistic, Convolutional, and Generative Networks), and supercomputing techniques (with a focus on MARCC) to address mechanical engineering-related machine learning problems. The course requires Python 3+ programming skills; a free 3-hour Python 3+ tutorial will be provided to those who need to learn Python. Prerequisite(s): ***Mechanical Engineering students only: Must complete core course first (EN.535.641 Mathematical Methods for Engineers).
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