Upcoming Events

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Events Archive

Machine Learning and the Physical World

Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling).

Location: Julius Romo Rabinowitz 399

Guest Lecture: Algorithms of Oppression: How Search Engines Reinforce Racism

In Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities.

Location: 010 Pine

Colloquium: Data-driven Models for the Physical Sciences

There is immense hype, and immense promise, in machine learning for physics and astronomy. I use the case of stellar astrophysics as an example area in which to explore these ideas. It is an ideal field, because there are both very large data sets and incredibly detailed and successful physical models. And yet these models are nonetheless...

Location: Peyton Hall , Room 145

Colloquium: Machine Learning at Facebook

Machine intelligence for processing big data sets is big business. A statistical mathematician's point of view has led to (1) effective large-scale principal component analysis and singular value decomposition, and (2) some theoretical foundations for convolutional networks (convolutional networks underpin the recent revolution in artificial...

Location: 214 Fine Hall

Graduate Certificate Information Session

Open to anyone interested in learning more about CSML's Graduate Certificate Program.  CSML Director Peter Ramadge will be available to answer questions.

Location: 26 Prospect Ave, Princeton NJ

Colloquium: Hamiltonian Descent Methods

We propose a family of optimization methods that achieve linear convergence using first-order gradient information and constant step sizes on a class of convex functions much larger than the smooth and strongly convex ones. This larger class includes functions whose second derivatives may be singular or unbounded at their minima.

Location: Computer Science Building Lecture Hall 104

Text Analysis Tools for Finding Emotion Behind Economic Outcomes

(To join for lunch, please register with an email to behavioralpolicy@princeton.edu)


Location: 301 Julis Romo Rabinowitz building

Seminar: Stochastic Gradient and Mirror Descent: Minimax Optimality and Implicit Regularization

Abstract: Stochastic descent methods (of the gradient and mirror varieties) have become increasingly popular in optimization.

Location: B205 Engineering Quadrangle

Fundamentals of Deep Learning for Computer Vision with NVIDIA

This workshop teaches you to apply deep learning techniques to a range of computer vision tasks through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows to train and deploy neural network models on a fully-configured, GPU accelerated workstation in the cloud.

Location: PCTS, 407 Jadwin Hall