Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter. Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as “AI winters.”
This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as “convolutional neural networks.” This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: “Deep Learning.” Few people have been more closely associated with Deep Learning than Yann LeCun, 54. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. E0 370: Statistical Learning Theory, August Term 2013. August - December 2013 Department of Computer Science & AutomationIndian Institute of Science [Course Description] [Announcements] [Assignments] [Lectures] Course Information Instructor: Prof.
Shivani Agarwal (shivani@csa) Meeting times/venue: Tu-Th 11:30am-1:00pm, CSA 252 (Multimedia Classroom) First class meeting: Tue Aug 6 Course Description This is an advanced course on learning theory suitable for PhD students working in learning theory or related areas (e.g. information theory, game theory, computational complexity theory etc) or 2nd-year Masters students doing a machine learning related project that involves learning-theoretic concepts. References: The course will not follow any single textbook; lecture notes will be made available online and pointers to relevant literature will be provided. L. 1.pdf. CS 229: Machine Learning Final Projects, Autumn 2012. Feature Column. In what follows, we will describe the work of Breiman and his colleagues as set out in their seminal book "Classification and Regression Trees.
" Theirs is a very rich story, and we will concentrate on only the essential ideas... Introduction It's easy to collect data these days; making sense of it is more work. This article explains a construction in machine learning and data mining called a classification tree. Let's consider an example. In the late 1970's, researchers at the University of California, San Diego Medical Center performed a study in which they monitored 215 patients following a heart attack.
Assuming the patients studied were representative of the more general population of heart attack patients, the researchers aimed to distill all this data into a simple test to identify new patients at risk of dying within 30 days of a heart attack. Classification trees Together, the four measurements form a measurement vector, where Comments? Growing the decision tree Summary References.
17 Great Machine Learning Libraries. After wonderful feedback on my previous post on Scikit-learn from the guys at /r/MachineLearning, I decided to collect the list of machine learning libraries into this seperate note.
Let me know if there’s a library that should be included here. Update (15 May 2014): thanks to Djalel Benbouzid and Dwayne Campbell for additional suggestions. Sorry it’s taken me so long to add them… Python Scikit-learn: comprehensive and easy to use, I wrote a whole article on why I like this library. Java Spark: Apache’s new upstart, supposedly up to a hundred times faster than Hadoop, now includes MLLib, which contains a good selection of machine learning algorithms, including classification, clustering and recommendation generation. Accord.NET: this seems to be pretty comprehensive, and comes recommended by primaryobjects on Reddit. Vowpal Wabbit: designed for very fast learning and released under a BSD license, this comes recommended by terath on Reddit.
General Conclusion Want more? 17 Great Machine Learning Libraries.