Machine Learning Theory
Machine Learning Theory
CS229M When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing a theoretical understanding of the statistical properties of learning algorithms. Topics Include: generalization bounds via uniform convergence, theory for deep learning, non-convex optimization, neural tangent kernel Implicit/algorithmic regularization, unsupervised learning and domain adaptation.
Level: Not defined
Certification: No
Cost: Free
Language: English
Type: Self-Paced
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