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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: Approved for both letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Programming Methods for Machine Learning. In this course you will learn how to use auto-differentiation tools like PyTorch, how to leverage them for basic machine learning algorithms (linear regression, logistic regression, deep nets, k-means clustering), and how to extend them with custom methods to fit your needs. Auto-differentiation tools are one of the most important tools for data analysis and a solid understanding is increasingly important in many disciplines. In contrast to existing courses which focus on algorithmic and theoretical aspects, here we focus on studying material that permits to deploy auto-diff tools to your area of interest. Prerequisites: ECE 220 and either MATH 286 or MATH 257.