Lecture 1 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: www.youtube.com CS 229 Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com



http://www.youtube.com/watch?v=UzxYlbK2c7E&hl=en

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Robert Full: Learning from the gecko's tail

www.ted.com Biologist Robert Full studies the amazing gecko, with its supersticky feet and tenacious climbing skill. But high-speed footage reveals that the gecko's tail harbors perhaps the most surprising talents of all.TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. Featured speakers have included Al Gore on climate change, Philippe Starck on design, Jill Bolte Taylor on observing her own stroke, Nicholas Negroponte on One Laptop per Child, Jane Goodall on chimpanzees, Bill Gates on malaria and mosquitoes, Pattie Maes on the "Sixth Sense" wearable tech, and "Lost" producer JJ Abrams on the allure of mystery. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http Watch a highlight reel of the Top 10 TEDTalks at www.ted.com



http://www.youtube.com/watch?v=d3syTrElgcg&hl=en

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Symmetry Group-based Learning for Regularity Discovery from Real World Patterns

Google Tech Talks December 15, 2008 ABSTRACT We explore a formal and computational characterization of real world regularity using discrete symmetry groups (hierarchy) as a theoretical basis, embedded in a well-defined Bayesian framework. Our existing work on "A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups" (TPAMI 2004), 'Near-regular texture analysis and manipulation' (SGIGRAPH 2004), and "A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking" (PAMI 2007) already demonstrate the power of such a formalization on a diverse set of real problems, such as texture analysis, synthesis, tracking, perception and manipulation in terms of regularity. Symmetry and symmetry group detection from real world data turns out to be a very challenging problem that has been puzzling computer vision researchers for the past 40 years. Our novel formalization will lead the way to a more robust and comprehensive algorithmic treatment of the whole regularity spectrum, from regular (perfect symmetry), near-regular (deviations from symmetry), to various types of irregularities. The recent results of the proposed methodology will be illustrated in this talk by several real world applications such as deformed lattice detection, rotation and glide-reflection detection, gait recognition, grid-cell clustering, symmetry of dance, automatic geo-tagging and image de-fencing. Speaker: Yanxi Liu Yanxi Liu received her BS degree in physics/electrical ...



http://www.youtube.com/watch?v=62nr29_zGZg&hl=en

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