Taskar Memorial Lecture 2018: M. Jordan (UC, Berkeley)
On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex Abstract: Many new theoretical challenges have arisen in the area of gradient-based optimization for large-scale statistical data analysis, driven by the needs of applications and the opportunities provided by new hardware and software platforms. I discuss several recent, related results in this area: (1) a new framework for understanding Nesterov acceleration, obtained by taking a continuous-time, Lagrangian/Hamiltonian/symplectic perspective, (2) a discussion of how to escape saddle points efficiently in nonconvex optimization, and (3) the acceleration of Langevin diffusion. Bio: Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. This video is closed captioned.
Ben Taskar Memorial Lecture: Daphne Koller (Coursera)
Michael I. Jordan: Machine Learning: Dynamical, Stochastic & Economic Perspectives
Bayesian or Frequentist, Which Are You? By Michael I. Jordan (Part 1 of 2)
The Data Science Revolution - Michael Jordan (UC Berkeley)
Fireside Chat with Michael Jordan
Britain Sold Palestine to Pay Its WWI Debt. The Balfour Declaration Was a Banking Deal!
AI Is Creating A Rare Opportunity For Investors. How Jim Roppel Is Playing It. | Investing With IBD
On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex
Four Ways of Thinking: Statistical, Interactive, Chaotic and Complex - David Sumpter
Principles For Human-Centered AI | Michael I Jordan (UC Berkeley)
Dynamical, symplectic and stochastic perspectives on optimization – Michael Jordan – ICM2018
Terence Tao: Nobody Understands Why AI Actually Works
Toward Total Scene Understanding for Autonomous Driving—Drago Anguelov (Waymo)
The weirdest paradox in statistics (and machine learning)
What Happens to Software When Proof is Cheap?
What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service
Surface Data vs. Deep Data
IST Austria Lecture "Computational thinking, inferential thinking & data science" by Michael Jordan
Nonparametric Bayesian Methods: Models, Algorithms, and Applications I
