Scientific Machine Learning: Applications and Algorithms
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Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
The session explains how momentum accelerates gradient descent, presents the theory of nonlinear conjugate gradients on manifolds, compares several beta‑rule updates, discusses line‑search and preconditioner choices, and shows how to employ the Manopt library for Riemannian optimization without explicit Hessians.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.