In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities, automated monitoring of activities, segmenting out organs or tissues in biological images, and sensing for control of robots.
Filosofisk text av Zach Barnett. Bra!
The following dialogue was published by Cambridge University Press: THINK29, Vol. 10 (Autumn 2011)
© 2011 Royal Institute of Philosophy All Rights Reserved
The following notes represent a complete, stand alone interpretation of Stanford’s machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester.
Past, Present, Future Vision of AI - Google and AAAI 2011
Google Tech Talk, August 9, 2011 Presented by Peter Norvig. ABSTRACT: Google hosted 100 attendees of the 2011 conference for the Association of the Advancement of Artificial Intelligence (AAAI) at our San Francisco office. The program showcased a featured talk from Director of Research Peter Norvig and a lightning talk series on an array of projects relevant to the field of artificial intelligence and its applications. About the speaker: Peter Norvig is a Director of Research at Google. He is a Fellow of the Association for Computing Machinery and the American Association for Artificial Intelligence and co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field.
Stanford has been offering portions of its robotics coursework online for a few years now, but professors Sebastian Thrun and Peter Norvig are kicking things up a notch (okay, lots of notches) with next semester’s CS221: Introduction to Artificial Intelligence. For the first time, you can take this course, along with several hundred Stanford undergrads, without having to fill out an application, pay tuition, or live in a dorm.
Gratis utbildning är det bästa.
… with four separate Neural Network sessions at this year’s ICML, it looks like Neural Networks are making a comeback
- /blog - A blog about building ML + NLP tools ;
- /consulting - If you want to hire a consultant on predictive analytics or business intelligence, contact me. I consult and I know a bunch of other people who do too ;
- /jobs - If you do ML + NLP and are interested in a gig, contact me. I get a lot of offers over the transom ;
- /projects/wordreprs - Word representations for NLP ;
Following the two previous successful conferences which took place in Edinburgh, Scotland, the third International Conference of Music and AI will take place in Rethymnon, Crete, Greece, between the 26th and 28th March 2011. This is a small-scale friendly conference with a small number of oral presentations and adequate time for discussions and other activities. We encourage submissions on all aspects of music involving Artificial Intelligence techniques and methodologies, as well as related theoretical issues.
Förmodligen en av de bästa böckerna på ämnet Informationsteori. Skriven av David J.C. MacKay. Gratis online.
The objective is to create a computer program that plays the game of Planet Wars as intelligently as possible.
Finns startpaket för Java, C++, C# och Python. Är smått sugen att kika lite närmare.
The ambition of this page is to be a comprehensive collection of links to papers describing RL algorithms. In order to make this list manageable we should only consider RL algorithms that originated a class of algorithms and have been used/studied by at least one researcher(s) unaffiliated with the original inventor(s) of the algorithm.
Inte kikat på än, men kanske kan vara en trevlig introduktion.
Survey several basic AI techniques implemented with short, open-source Python code recipes. Appropriate for educators and programmers who want to experiment with AI and apply the recipes to their own problem domains. For each technique, learn the basic operating principle, discuss an approach using Python, and review a worked out-example. We’ll cover database mining using neural nets, automated categorization with a naive Bayesian classifier, solving popular puzzles with depth-first and breath-first searches, solving more complex puzzles with constraint propagation, and playing a popular game using a probing search strategy.