# AI News, Simons Institute for the Theory of Computing

- On Wednesday, September 26, 2018
- By Read More

## Simons Institute for the Theory of Computing

final component of the program is understanding heuristics: what works in practice, and why. The most popular algorithms for a variety of basic statistical tasks—clustering, embedding, and so on—behave in a manner that is not fully understood. Some, like principal component analysis, have strong properties, but are used in ways that cannot directly be justified by appealing to these properties. Others, like k-means, have obvious failure modes in a worst-case setting, and yet are quite successful on many types of data. The program will bring together theoreticians and practitioners who are interested in teasing apart these issues and in expanding the useful formal characterizations of such procedures.

- On Wednesday, September 18, 2019

**Lecture 03 -The Linear Model I**

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**Economic Calculation in a Natural Law / RBE, Peter Joseph, The Zeitgeist Movement, Berlin**

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**On Intrinsic Rewards and Continual Learning**

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**Introduction to the class and overview of topics**

MIT 8.591J Systems Biology, Fall 2014 View the complete course: Instructor: Jeff Gore In this lecture, Prof. Jeff Gore introduces the ..

**Ses 3: Present Value Relations II**

MIT 15.401 Finance Theory I, Fall 2008 View the complete course: Instructor: Andrew Lo License: Creative Commons BY-NC-SA ..

**Mod-01 Lec-39 Genetic Algorithms contd...**

Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit ...

**A Framework for Interactive Learning**

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**NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Hazy - Making Data-driven...**

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Hazy: Making Data-driven Statistical Applications ...