Undergraduate Prerequisites: (ENGEC414 & ENGEK381 & ENGEK103) - Efficient algorithms to train large models on large datasets have been critical to the recent successes in machine learning and deep learning. This course will introduce students to both the theoretical principles behind such algorithms as well as practical implementation considerations. topics include convergence properties of first-order optimization technologies such as stochastic gradient descent, with particular focus on optimization problems with non-convex losses typically present in modern deep learning problems. After completing this course, students should be able to read, and understand optimization algorithms from literature as well as design and implement new optimization algorithms.

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