Lecture 3 | Learning, Empirical Risk Minimization, and Optimization Published 2019-09-04 Download video MP4 360p Recommendations 1:09:13 Lecture 1 | The Perceptron - History, Discovery, and Theory 40:08 The Most Important Algorithm in Machine Learning 1:17:41 Lecture 2 | The Universal Approximation Theorem 46:33 Machine Learning Lecture 16 "Empirical Risk Minimization" -Cornell CS4780 SP17 47:27 Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering 1:19:04 Lecture 5 | Convergence, Learning Rates, and Gradient Descent 17:38 The moment we stopped understanding AI [AlexNet] 1:09:57 Quantum Computing and the Limits of the Efficiently Computable - 2011 Buhl Lecture 21:44 The weirdest paradox in statistics (and machine learning) 21:08 Manifold Mixup: Better Representations by Interpolating Hidden States 1:34:41 Reinforcement Learning 6: Policy Gradients and Actor Critics 55:55 Miles Cranmer - The Next Great Scientific Theory is Hiding Inside a Neural Network (April 3, 2024) 1:20:23 Course Introduction and Overview: Graduate Complexity Lecture 1 at CMU 25:28 Watching Neural Networks Learn 34:48 The Unreasonable Effectiveness of JPEG: A Signal Processing Approach Similar videos 10:28 Neural networks [2.1] : Training neural networks - empirical risk minimization 1:25:44 11-785 Spring 23 Lecture 4: Empirical Risk Minimization and Gradient Descent 23:50 Statistical Machine Learning Part 6 - Risk minimization, approximation and estimation error 12:37 I2ML - ML Basics - Losses & Risk Minimization 1:26:03 Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018) 14:25 Empirical Risk Minimization - Machine Learning 02 More results