01-Model, sufficient statistics, Setup for Neyman Pearson, Bayesian, Minmax
02-Cramer Rao and ML Lecture 2
03-Solving for NP, Bayesian Lecture 3
04-Sequential detection Lecture 4
05-Estimation, UMVU, different loss functions Lecture 5
06-Stein’s method Lecture 6
07-Foundations of Machine Learning and Statistical Inference Lecture 7
08-Foundations of Machine Learning and Statistical Inference Lecture 8
09-Foundations of Machine Learning and Statistical Inference Lecture 9
10-Foundations of Machine Learning and Statistical Inference Lecture 10
11-Foundations of Machine Learning and Statistical Inference Lecture 11
12-Foundations of Machine Learning and Statistical Inference Lecture 12
13-Foundations of Machine Learning and Statistical Inference Lecture 13
14-Foundations of Machine Learning and Statistical Inference Lecture 14
15-Foundations of Machine Learning and Statistical Inference Lecture 15
16-Big data is low rank - Madeleine Udell, Cornell University
17-A function space view of overparameterized neural networks
18-Approximate cross validation for large data and high dimensions - Tamara Br
19-From causal inference to autoencoders, memorization & gene regulation - Car
20-Benign overfitting- Peter Bartlett, UC Berkley
21-Can learning theory resist deep learning_ Francis Bach, INRIA
22-Fast and optimal low-rank tensor regression via importance - Garvesh Raskut
23-Does learning require memorization_ A short tale about a long tail - Vitaly
24-Implicit regularization for general norms and errors - Lorenzo Rosasco, MIT
25-Statistical Physics and Learning - Florent Krzakala, Sorbonne Université
