pinouchon.github.io
The appeal for probabilistic models
http://pinouchon.github.io/ai/dl/2016/08/08/The-appeal-for-probabilistic-models.html
The appeal for probabilistic models. Aug 8, 2016. Probabilitsic models of cognition is a truly great idea. It is the kind we need in AI in order to make tangible progress. Side note: because the content is not easily accessible, few people can appreciate how great the content is, and this is a bit sad. In Theory-based Bayesian models of inductive learning and reasoning. Joshua B. Tenenbaum, Thomas L. Griffiths, and Charles Kemp explain:. They consider the general problem that Tenenbaum often refers to as.
blog.learnstream.org
What I’m learning – 8/5/14 | Learnstream
http://blog.learnstream.org/2014/08/what-im-learning-8514
Designing technology as an environment where natural learning flourishes. Skip to primary content. Skip to secondary content. So Good They Can’t Ignore You. What I’m learning – 8/5/14. August 6, 2014. Learning How to Learn. Online book) I recently did CIS194: Introduction to Haskell. Probabilistic Models of Cognition. Why Do Americans Stink at Math? Article, NY Times) There is a ringing endorsement among those who are good at math: “don’t just memorize a procedure,. Another fascinating possibility for te...
projects.csail.mit.edu
Church Wiki
http://projects.csail.mit.edu/church/wiki/Church
Church is a probabilistic programming language. Designed for expressive description of generative models (Goodman, Mansinghka, Roy, Bonawitz and Tenenbaum, 2008). Church is a derivative of the programming language Scheme. With probabilistic semantics. This website serves as a portal to work related to Church, tutorials, reference implementations and a repository of probabilistic models expressed in Church. Probabilistic Models of Cognition. Older tutorials: Old PMC Tutorial. Many probabilistic models are...
dippl.org
Introduction
http://dippl.org/chapters/01-introduction.html
Of Probabilistic Programming Languages. Programming languages (PPLs). These languages provide compositional means for describing complex probability distributions; implementations of these languages provide. Computable distribution can be represented as the distribution induced by a Church program in this way (see Freer and Roy, 2012, and citations therein). Next chapter: The WebPPL language. Parts of the following description of probabilistic programming languages are taken from Goodman 2013.
josephjaywilliams.com
Teaching & Mentoring - Joseph Jay Williams
http://www.josephjaywilliams.com/teachingmentoring
More Research and Applications. News, Events and Blog. Modularity and MOOClets Working Group. Rapid Authoring of MOOClets/Lessons/Exercises. Online Learning Authoring Tools. Creating Resources with Google Apps. Experiments and Online Education. Events relevant to Online Education. Collaborating using Online Educational Resources. CHI 2014 Course: Conducting Online Experiments. Resources: Software and Research. Software: Web Development, Collaboration, Productivity. How to perform multi-stage experiments.
moalquraishi.wordpress.com
The State of Probabilistic Programming « Some Thoughts on a Mysterious Universe
https://moalquraishi.wordpress.com/2015/03/29/the-state-of-probabilistic-programming
Some Thoughts on a Mysterious Universe. The State of Probabilistic Programming. March 29, 2015. For two weeks last July, I cocooned myself in a hotel in Portland, OR, living and breathing probabilistic programming as a student in the probabilistic programming summer school. The school is part of the broader DARPA program on Probabilistic Programming for Advanced Machine Learning (PPAML). Which did a terrific job of organizing the event. Thankfully, they’re hosting the summer school again this year. Befor...
dk-techlogic.blogspot.com
TECH LOGIC: Machine Learning Resources for Beginners and Beginners++
http://dk-techlogic.blogspot.com/2012/05/best-machine-learning-resources.html
Machine Learning Resources for Beginners and Beginners. I have been learning ML for sometime now and I have spent some time on finding what are some of the good resources for ML. For Beginners. Compiling mex files on 64bit Linux(UBUNTU) using MATLAB 2012. I could not setup the compiler for myself to create mex files. So I searched the internet, going through many blogs and posts. Finally I go. Unsupervised Feature Learning and Deep Learning Resources. Setting up Theano on Ubuntu 14.04. Before starting I...
