Complete Statistical Theory of Learning (Vladimir Vapnik) | MIT Deep Learning Series

Lecture by Vladimir Vapnik in January 2020, part of the MIT Deep Learning Lecture Series.
Slides: http://bit.ly/2ORVofC
Associated podcast conversation: https://www.youtube.com/watch?v=bQa7hpUpMzM
Series website: https://deeplearning.mit.edu
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:00 – Introduction
0:46 – Overview: Complete Statistical Theory of Learning
3:47 – Part 1: VC Theory of Generalization
11:04 – Part 2: Target Functional for Minimization
27:13 – Part 3: Selection of Admissible Set of Functions
37:26 – Part 4: Complete Solution in Reproducing Kernel Hilbert Space (RKHS)
53:16 – Part 5: LUSI Approach in Neural Networks
59:28 – Part 6: Examples of Predicates
1:10:39 – Conclusion
1:16:10 – Q&A: Overfitting
1:17:18 – Q&A: Language

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