## 2019

- December 2 & 9, 2019,
**Variational Inference: Foundations and Innovations**(Part 1), David Blei (Columbia University) - November 18,
**On Large Deviation Principles for Large Neural Networks**, Joan Bruna (Courant Institute of Mathematical Sciences, NYU) - November 11, 2019,
**Anomaly Detection using Neural Networks**, Dean Langsam (BlueVine) - October 28 & November 4, 2019,
**Extreme Value Theory**. Paul Embrechts (ETH) - October 7, 2019,
**On the Optimization Landscape of Matrix and Tensor Decomposition Problems**, Tengyu Ma (Princeton University) - September 30, 2019,
**Recurrent Neural Networks**, Ava Soleimany (MIT) - September 23, 2019,
**When deep learning does not learn,**Emmanuel Abbe (EPFL and Princeton) - July 15, 2019,
**Optimality in Locally Private Estimation and Learning**, John Duchi (Stanford) - July 1, 2019.
**Capsule Networks**, Geoffrey Hinton (University of Toronto – Google Brain – Vector institute) - June 24, 2019,
**A multi-perspective introduction to the EM algorithm**, William M. Wells III. - June 17, 2019,
**Theoretical Perspectives on Deep Learning,**Nati Srebro (TTI Chicago) - May 27, 2019.
**2018 ACM Turing Award**. Stanford Seminar –**Human in the Loop Reinforcement Learning**. Emma Brunskill (Stanford) - May 20, 2019.
**How Graph Technology Is Changing Artificial Intelligence and Machine Learning**. Amy E. Hodles (Neo4j), Jake Graham (Neo4j). - May 13, 2019,
**2017 Nobel Lectures in Physics**. Awarded « for decisive contributions to the LIGO detector and the observation of gravitational waves ». Rainer Weiss (MIT), Barry C. Barish (Caltech) and Kip S. Thorne (Caltech) - May 6, 2019,
**Accessorize to a Crime: Real and Stealthy Attacks on State-Of-The-Art Face Recognition**, Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer (Carnegie Mellon University) and Michael K. Reiter (University of North Carolina Chapel Hill), paper - April 29, 2019,
**Build Intelligent Fraud Prevention with ML and Graphs**, Nav Mathur, Graham Ganssle - April 15, 2019,
**Active Learning: Why Smart Labeling is the Future of Data Annotation**, Jennifer Prendki (Figure Eight) - April 8, 2019,
**Generalization, Interpolation, and Neural Nets**, Alexander Rakhlin (MIT) - April 1, 2019,
**Similarity learning using deep neural networks**– Jacek Komorowski (Warsaw University of Technology) - March 18/25, 2019,
**Deep Reinforcement Learning (****First lecture of MIT course 6.S091)**, Lex Fridman (MIT) - March 11, 2019,
**Ensembles: Boosting**, Alexander Ihler University of California, Irvine) - March 4, 2019,
**Dataset shift in machine learning**, Peter Prettenhofer (DataRobot) - February 25, 2019,
**Could Machine Learning Ever Cure Alzheimer’s Disease?**– Winston Hide (Sheffield University) - February 18, 2019,
**2015 IAAA Winner Intelligent Surgical Scheduling System** - February 11, 2019,
**Artificial Intelligence Machine Learning Big Data, Exponential Finance**– Neil Jacobstein (Singularity University) - February 4, 2019,
**Bayesian Deep Learning with Edward (and a trick using Dropout)**– Andrew Rowan (PrismFP) - January 28, 2019,
**Ouroboros,**Aggelos Kiayias (University of Edinburgh) - January 21, 2019,
**Cosmos Proof of Stake**– Sunny Aggrawal - January 14, 2019,
**Geometric Deep Learning**– Michael Bronstein (University of Lugano and Tel Aviv University) - January 7, 2019,
**Deep Generative Networks as Inverse Problems**– Stéphane Mallat, Ecole Normale Supérieure (ENS)

## 2018

- December 3/17, 2018,
**Convex Optimization and Applications**– Stephen Boyd (Stanford University) - November 26, 2018,
**The mathematics of machine learning and deep learning**– Sanjeev Arora (Princeton University) - November 15, 2018,
**Reinforcement Learning in Healthcare: Challenges and Promise –**Doshi-Velez (Harvard University)