Part 1:  Introduction to some tools that computer programmers typically use to write and debug code in an
 effective manner.

Part 2:  Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.

Part 1:  Introduction to some tools that computer programmers typically use to write and debug code in an
 effective manner.

Part 2:  Introduction to cloud computing (create and manage cloud computing resources) How to use some tools that offer the possibility of writing code locally while seamlessly executing/running it on powerful cloud computing.

The Center for Statistics and Machine Learning (CSML) is offering a three-hour Wintersession workshop, which aims to increase awareness of how machine learning could aid faculty, postdoc, and student research.

No detailed prior knowledge of machine learning is assumed. The workshop will begin with an overview of crucial…

This mini-course will provide a comprehensive introduction to machine learning. Part 1 will briefly overview the full machine learning process and cover introductory concepts such as what is machine learning and why is it used. Popular software libraries will be discussed. Attendees will begin working hands-on in Part 2 to train simple machine learning models. Part 3 covers model evaluation and refinement. Artificial neural networks are introduced during Part 4. The mini-course concludes with a hackathon during Part 5 where participants will work on a small, end-to-end machine learning project chosen from one of multiple domains.

In this talk I will first describe our work on developing new tools for screening and intervention in developmental disorders, autism spectrum disorder and eating disorders in particular. I will show how equipped with computer vision and machine learning, we deployed scalable, phone/tablet-based tools in pediatric clinics and homes in the US and Africa.

What will philology become in the wake of the digital revolution? How can computer vision, handwritten text recognition, natural language processing, deep neural networks and/or other forms of machine learning refine the arsenal of techniques for studying premodern evidence?

This works-in-progress symposium will feature six teams of Princeton scholars who are applying machine learning to manuscripts, rare books, archives, inscriptions, coins and other pre-1600 texts. Presentations will include projects on materials in Syriac, Hebrew, Latin, Greek, Chinese and English. 

Are you curious about how machine learning can be used to study fragments of medieval Egyptian letters? Or how quantitative methods can help trace the monetization of misinformation on the web? Intrigued by AI but don’t know what it is? Not sure how to work with your complex collection of texts, images, and other media? Want to learn coding but don’t know where to start? 

Risk-averse optimization plays a major role in the design of safety for machine learning applications. In this talk, we will present a set of tools to enhance the robustness of models and algorithms to potentially harmful data shifts.

Lunch from 12:15 p.m., RSVP to [email protected]

In the context of first-order algorithms subject to random gradient noise, we study the trade-offs between the convergence rate (which quantifies how fast the initial conditions are forgotten) and the "risk" of suboptimality, i.e., deviations from the expected suboptimality.

Lunch from 12:15 p.m., RSVP to [email protected]

Sparse statistical estimators are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, sparse estimation problems with an L0 constraint, restricting the support of the estimators, are challenging (typically NP-hard, but not always) non-convex optimization problems.