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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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. 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 CCS 229 Course Website: www.stanford.edu Stanford University: www.stanford.edu Stanford University Channel on YouTube: www.youtube.com

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Stanford University will be offering a free, online machine learning class in Fall 2011, taught by Prof. Andrew Ng. Sign up at ml-class.org

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(May 21, 2011) Andrew Ng (Stanford University) is building robots to improve the lives of millions. From autonomous helicopters to robotic perception, Ng's research in machine learning and artificial intelligence could result one day in a robot that can clean your house. STAN: Society, Technology, Art and Nature, was Stanford University's prototype conferecne for TEDxStanford, and showcased some of the university's top faculty, students, alumni and performers in an intense four-hour event laced with surprising appearances and memorable experiences. STAN, modeled after TED, explored big questions about society, technology, art and nature in a format that invites feedback and engagement. Stanford University: www.stanford.edu STAN 2011: stan2011.stanford.edu Andrew Ng ai.stanford.edu Stanford University Channel on YouTube: www.youtube.com

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The creation of intelligent computing systems that perceive, learn, and reason has been a long-standing and visionary goal in computer science. Over the last 20 years, technical and infrastructural developments have come together to create a nurturing environment for developing and fielding applications of machine learning and reasoning--and for harnessing machine intelligence to provide value to businesses and to people in the course of their daily lives. Key advances include jumps in the availability of rich streams of data, precipitous drops in the cost of storing and retrieving large amounts of data, increases in computing power and memory, and jumps in the prowess of methods for performing machine learning and reasoning. The combination of these advances have created an inflection point in our ability to harness data to generate insights and to guide decision-making. This talk will present recent efforts on learning and inference, highlighting key ideas in the context of applications, including advances in transportation and health care, and the development of new types of applications and services. Opportunities for creating systems with new kinds of competencies by weaving together multiple data sources and models will also be discussed.

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Matt Spitz There are a plethora of options when it comes to deciding how to add a machine learning component to your python application. In this talk, I'll discuss why python as a language is well-suited to solving these particular problems, the tra

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In this video tutorial from PyData Workshop, Jacob VanderPlas is going to give you an overview of machine learning in Python using scikit-learn. He'll talk about general machine learning concepts, as well as walk you through a few exorcises that demonstrate how you can use machine learning technology. **More tutorials on open source development at marakana.com

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. 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

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In this video from the Northeast Scala Symposium, Andrew McCallum, Professor of Computer Science at University of Massachusetts Amherst, is going discuss trends in machine learning using Scala. Martin Odersky didn't initially expect Scala to find a following in the field of machine learning because of machine learning's large appetite for memory and numeric computation. But the field is expanding in new ways, with interest in parallel and distributed computation, dynamically changing model structures, and the desire to put easy-to-use DSLs into the hands of non-experts. This talk will describe these trends and discuss several machine learning projects that use Scala, including FACTORIE, a 30k-line DSL for graphical models whose development is being sponsored by Google and the NSF.

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ai-one's Topic-Mapper API enables programmers to build machine learning into applications. ai-one has discovered a new form of artificial intelligence that detects the inherent structure of data at the byte-level.

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Attempt at a definition, and some applications of machine learning. A playlist of these Machine Learning videos is available here: www.youtube.com

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Bay Area Vision Meeting (more info below) Unsupervised Feature Learning and Deep Learning Presented by Andrew Ng March 7, 2011 ABSTRACT Despite machine learning's numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed "unsupervised feature learning" and "deep learning" algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature representation. These methods have also surpassed the previous state-of-the-art on a number of problems in vision, audio, and text. In this talk, I describe some of the key ideas behind unsupervised feature learning and deep learning, describe a few algorithms, and present case studies pertaining. The Bay Area Vision Meeting (BAVM) is an informal gathering (without a printed proceedings) of academic and industry researchers with interest in computer vision and related areas. The goal is to build community among vision researchers in the San Francisco Bay Area, however, visitors and travelers from afar are also encouraged to attend and present. New <b>...</b>

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Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Machine Learning and Hadoop by Josh Wills Abstract: We'll review common use cases for machine learning and advanced analytics found in our customer base at Cloudera and ways in which Apache Hadoop supports these use cases. We'll then discuss upcoming developments for Apache Hadoop that will enable new classes of applications to be supported by the system.

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SPEAKER & AFFILIATION: Thomas G. Dietterich, Oregon State University Corvalis DESCRIPTION: This lecture has been videocast from the Computer Science Department at Duke U. The abstract of this lecture and a brief speaker biography is available at research.csc.ncsu.edu To download a full transcript of this video, please follow: ncsu.edu

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(March 31, 2010) Venkat Rangan, a hardware engineer at Qualcomm Incorporated, discusses hardware, software, and networking challenges that humans will face in a creating a neuromorphic computer. Stanford University: www.stanford.edu Stanford School of Engineering: soe.stanford.edu Stanford Engineering Everywhere: see.stanford.edu Stanford University Channel on YouTube: www.youtube.com

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Ever wonder how netflix can predict what rating you would give to a movie? How do recommendation engines get built? Well, it's possible with JRuby and it's fairly straight forward. Many engines are built purely on support vector machine regressions which map arrays of data onto a classifier, like a star. In this talk I'll explain how support vector machines are built, and how do make a simple movie prediction model all in JRuby.

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Overview of ai-one Topic-Mapper SDK for building machine learning applications with lightweight ontologies.

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International Music and Machine Learning Workshop: Learning from Musical Structure at NIPS 2011 Invited Talk: A Topic Model for Melodic Sequences by Athina Spiliopoulou Athina is a PhD student in the Machine Learning group of the Institute for Adaptive and Neural Computation at the School of Informatics,University of Edinburgh. She works with Amos Storkey on Machine Learning methods for music, with a specific interest in unsupervised learning of musical structure from melodic sequences.

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Lecture notes: learning.stat.purdue.edu Large-scale Machine Learning and Stochastic Algorithms During the last decade, data sizes have outgrown processor speed. We are now frequently facing statistical machine learning problems for which datasets are virtually infinite. Computing time is then the bottleneck. The first part of the lecture centers on the qualitative difference between small-scale and large-scale learning problem. Whereas small-scale learning problems are subject to the usual approximation--estimation tradeoff, large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways. Unlikely optimization algorithm such as stochastic gradient show amazing performance for large-scale machine learning problems. The second part makes a detailed overview of stochastic learning algorithms applied to both linear and nonlinear models. In particular I would like to spend time on the use of stochastic gradient for structured learning problems and on the subtle connection between nonconvex stochastic gradient and active learning. See other lectures at Purdue MLSS Playlist: www.youtube.com

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Computers struggle with tasks we find simple. But try to describe explicitly the difference between the handwritten numerals 1 and 7, and you begin to appreciate the problem. Professor Martin Anthony explains what role mathematicians play in making computers less stupid. Diagnosing tumours, playing video games, detecting credit card fraud, recognising faces, reading handwriting they dont seem like similar tasks, but they are all cases where 'machine learning' is employed to enable computers to make intelligent decisions. And although the various tasks look very different, the mathematics behind them is remarkably similar, as Professor Martin Anthony explains in this short film. When computers fail to do something we find easy reading handwriting, recognising faces its tempting to think of them as stupid machines. But its often the case that tasks we find relatively easy to perform evade explicit codification. How, for example, would you specify rules which correctly identified cats and only cats including three-legged cats but excluded dogs? Employing ideas from probability theory, statistics, linear algebra, geometry and discrete mathematics, machine learning aims to generate systems of instructions algorithms that allow computers to perform cognitive-style tasks. In abstract terms, machine learning involves detecting patterns in very large datasets, clustering together similar objects and distinguishing dissimilar ones. This could help with the detection of anomalies <b>...</b>

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Google Tech Talk (more info below) June 1, 2011 Presented by Scott Davidoff, Ph.D. ABSTRACT This talk describes how my work with busy families: (1) identifies how their coordination breaks down (from 3 years of fieldwork and experience prototyping) (2) Identifies how we can apply unsupervised machine learning to this problem context (using mobile phone GPS) (3) Creates a new way to visualize the calendar that combines manually input and learned information

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Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Big Machine Learning made Easy by Miguel Araujo Miguel Araujo holds a BS and MS in computer science from Universidad Antonio de Nebrija and San Diego State University. He is a Machine Learning addict and an active open source hacker that enjoys coding in Python. Miguel is a contributor in open source projects like: django-rules, django-crispy-forms, and requests-oauth. Abstract: While machine learning has made its way into certain industrial applications, there are many important real-world domains, especially domains with large-scale data, that remain unexplored. There are a number of reasons for this, and they occur at all places in the technology stack.

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Unedited Lectures from the CS281B class in UC Berkeley More details (slides, assignments, scribe notes) can be found at alex.smola.org

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. 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

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What you will learn in the Stanford free online machine learning class in Fall 2011. Sign up at ml-class.org

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Google Tech Talks March, 25 2008 ABSTRACT SVN Vishwanathan - Research Scientist Regularized risk minimization is at the heart of many machine learning algorithms. The underlying objective function to be minimized is convex, and often non-smooth. Classical optimization algorithms cannot handle this efficiently. In this talk we present two algorithms for dealing with convex non-smooth objective functions. First, we extend the well known BFGS quasi-Newton algorithm to handle non-smooth functions. Second, we show how bundle methods can be applied in a machine learning context. We present both theoretical and experimental justification of our algorithms. Speaker: SVN Vishwanathan - Research Scientist - Zurich SVN Vishwanathan is a principal researcher in the Statistical Machine Learning program, National ICT Australia with an adjunct appointment at the College of Engineering and Computer Science(CECS), Australian National University. I got my Ph.D in 2002 from the Department of Computer Science and Automation (CSA) at the Indian Institute of Science.

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IBM's Watson is a real time, natural language processing computer that employs deep analytics and machine learning capabilities to answer questions. Watson's ability will be tested on the game show Jeopardy! Visit ibmwatson.com for more information.

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Believe it or not, computers have limits on what they can do for us. Professor Sandra Zilles at the University of Regina explains.

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. 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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. 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

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Unit 5 2 What is Machine Learning

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The experiment database for machine learning - expdb.cs.kuleuven.be - allows you to browse the results of millions of data mining experiments, with hundreds of data mining algorithms. This tutorial video explains how to use the graphical query interface and simple visualizations.

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Using fMRI scans, we can accurately determine 9 out of 10 times which of two words a person is thinking, just by looking at the pattern of brain activity.

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. 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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). 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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection. 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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. 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

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This will be a series of lectures on the subject of Machine Learning. The target audience are people who are not in this area of research, but are interested in the subject. Machine Learning is a HOT topic in Computer Science these days. Let's see what all the fuss is about!

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. 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

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Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. 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

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