EC525: Optimization for Machine Learning (Fall 2023)

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 techniques such as stochastic gradient descent, adaptive learning rate schemes, and momentum. Particular focus will be given to the stochastic optimization problems with non-convex loss surfaces typically present in modern deep learning problems.

Syllabus with meeting time and other logistical information (BU login required)



Ability to program in Python. Comfort with linear algebra, calculus, and probability. Example concepts that should be familiar include gradients, eigenvectors, eigenvalues, Taylor series, and expectations. The class will require writing rigorous mathematical proofs.

Course Notes available here

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This course involves a lot of proofs, both in lecture and on the homework. If you're not confident about proof-writing, these exercises may provide some practice. These are "general" abstract math problems are not particularly related to the content of the course other than requiring proof writing.