machinelearningsalon kit 28-12-2014

machinelearningsalon kit 28th December 2014 Dont keep an old version! machinelearningsalon kit is regularly updated!

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machinelearningsalon.org !"#$%"$&($%)($*+,)-.($/(+0.-.1$2+3".4$5-%6$77777777777777777777777777777777777777777777777777777777777$89 What is the Machine Learning Salons Kit? .................................................................................................. 16 What is not the Machine Learning Salons Kit? .......................................................................................... 16 Why are we not on GitHub? ................................................................................................................................ 16 If you are a CTO who wants to recruit smart Machine Learning developers ............................... 16 If you want to become a contributor .............................................................................................................. 16 If you want to remove a link .............................................................................................................................. 16 If you want to add a better description of your website ........................................................................ 16 If you are willing to give a discount to the Machine Learning Salons readers ............................ 17 About the Founder of The Machine Learning Salons Website & Kit ............................................... 17 Contact ......................................................................................................................................................................... 17 *::;$"0$:


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Francis Bach, Ecole Normale Superieure - Courses and Exercises with solutions (English-French) ........................................................................................................................................................................ 27 Technion, Israel Institute of Technology, Machine Learning Videos ............................................... 28 NPTEL, National Programme on Technology Enhanced Learning, India ....................................... 28 Probability Theory and Applications .............................................................................................................. 28 Pattern Recognition ............................................................................................................................................... 28

Videolectures.net .................................................................................................................................................... 28 MLSS Machine Learning Summer Schools Videos .................................................................................... 28 MLSS Videos from 2004 to 2012 ....................................................................................................................... 28 MLSS Videos 2012 ................................................................................................................................................... 28 MLSS Videos 2012 ................................................................................................................................................... 28 Max Planck Institute for Intelligent Systems Tubingen, MLSS Videos 2013 .................................. 29

GoogleTechTalks ..................................................................................................................................................... 29 Machine Learning ................................................................................................................................................... 29 Deep Learning ........................................................................................................................................................... 29

Udacity Opencourseware .................................................................................................................................... 29 Supervised Learning (select "View Courseware" for free access) ..................................................... 29 Unsupervised Learning (select "View Courseware" for free access) ................................................ 29 Reinforcement Learning (select "View Courseware" for free access) ............................................. 30

Mathematicalmonk Machine Learning .......................................................................................................... 30 Judea Pearl Symposium ....................................................................................................................................... 30 Machine Learning Reading Group, Indian Institute of Science ........................................................... 30 SIGDATA, Indian Institute of Technology Kanpur .................................................................................... 30 Hakka Labs ................................................................................................................................................................. 30 Open Yale Course .................................................................................................................................................... 31 Columbia University .............................................................................................................................................. 31 Machine Learning resources .............................................................................................................................. 31 Applied Data Science by Ian Langmore and Daniel Krasner ............................................................... 31

Deep Learning .......................................................................................................................................................... 32 BigDataWeek Videos ............................................................................................................................................. 32 Neural Information Processing Systems Foundation (NIPS) Video resources ............................ 32 Hong Kong Open Source Conference 2013 (English&Chinese) .......................................................... 33 ICLR 2014 Videos .................................................................................................................................................... 33 ICLR 2013 Videos .................................................................................................................................................... 33 Machine Learning Conference Videos ........................................................................................................... 33 Internet Archive ...................................................................................................................................................... 35 University of Berkeley .......................................................................................................................................... 35 AMP Camps, Big Data Bootcamp, UC Berkeley ........................................................................................... 35 Resources and Tools of Noah's ARK Research Group ............................................................................. 35 ESAC DATA ANALYSIS AND STATISTICS WORKSHOP 2014 ............................................................... 36 The Royal Society .................................................................................................................................................... 36 Statistical and causal approaches to machine learning by Professor Bernhard Schlkopf ... 37

Deep Learning .......................................................................................................................................................... 37 Deep Learning RNNaissance with Dr. Juergen Schmidhuber .............................................................. 37 Introduction to Deep Learning with Python by Alec Radford ............................................................. 37

Miscellaneous ........................................................................................................................................................... 38 Introduction To Modern Brain-Computer Interface Design by Swartz Center for Computational Neuroscience ............................................................................................................................. 38 Distributed Computing Courses (lectures, exercises with solutions) by ETH Zurich, Group of Prof. Roger Wattenhofer ...................................................................................................................................... 38


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The wonderful and terrifying implications of computers that can learn | Jeremy Howard | TEDxBrussels ............................................................................................................................................................. 39

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Visualizing MBTA Data: An interactive exploration of Boston's subway system ....................... 46

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OCTAVE ....................................................................................................................................................................... 54 JULIA ............................................................................................................................................................................. 55 Julia by example ....................................................................................................................................................... 55

The R PROJECT for Statistical Computing .................................................................................................... 55 R ...................................................................................................................................................................................... 55 R Graph Gallery ........................................................................................................................................................ 55 Code School - R Course .......................................................................................................................................... 56 Coursera R programming .................................................................................................................................... 56 Open Intro R Labs .................................................................................................................................................... 56 R Tutorial .................................................................................................................................................................... 56 DataCamp R Course ................................................................................................................................................ 56 R Bloggers ................................................................................................................................................................... 56

STAN Software ......................................................................................................................................................... 57 List of Machine Learning Open Source Software ...................................................................................... 57 Google Prediction API ........................................................................................................................................... 57 Reddit ........................................................................................................................................................................... 58 SCHOGUN toolbox ................................................................................................................................................... 58 Infer.NET, Microsoft Research .......................................................................................................................... 58 F# Software Foundation ...................................................................................................................................... 58 BigML ........................................................................................................................................................................... 59 BRML Toolbox in Matlab David Barber Toolbox, University College London .......................... 59 Dmitry Efimov Software ...................................................................................................................................... 59 SCILAB ......................................................................................................................................................................... 59 OverFeat and Torch7, CILVR Lab @ NYU ..................................................................................................... 59 Mloss.org .................................................................................................................................................................... 59 Sourceforge ............................................................................................................................................................... 60 Freecode ..................................................................................................................................................................... 60 Open Machine Learning Workshop organized by Alekh Agarwal, Alina Beygelzimer, and John Langford, August 2014 ............................................................................................................................... 60 Maxim Milakov Software ..................................................................................................................................... 60 Alfonso Nieto-Castanon Software .................................................................................................................... 61 Lib Skylark ................................................................................................................................................................. 61 Mutual Information Text Explorer .................................................................................................................. 61

Data Science Resources by Jonathan Bower on GitHub ......................................................................... 61 Joseph Misiti's Blog ................................................................................................................................................ 62 Michael Waskom GitHub repositories ........................................................................................................... 62 Visualizing distributions of data ...................................................................................................................... 62

Exploring Seaborn and Pandas based plot types in HoloViews by Philipp John Frederic Rudiger ........................................................................................................................................................................ 62 Open Source Hong Kong ...................................................................................................................................... 63 Lamda Group, Nanjing University ................................................................................................................... 63

U-1$V+%+W;3"&P$;"D.13-)$77777777777777777777777777777777777777777777777777777777777777777777777777$9A Apache SPARK .......................................................................................................................................................... 63 Apache Spark Machine Learning Library ..................................................................................................... 63 2013 Spark Summit exercises ............................................................................................................................ 63 2014 Spark Summit Training ............................................................................................................................. 64 Apache Spark Summit Videos ............................................................................................................................ 64 Databricks Videos .................................................................................................................................................... 64

Apache MAHOUT ..................................................................................................................................................... 65


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Apache Mahout ML library ................................................................................................................................. 65 Apache Mahout on Javaworld ............................................................................................................................ 65

Deeplearning4j ......................................................................................................................................................... 65 Udacity opencourseware "Intro to Hadoop and MapReduce" ............................................................ 66 Storm Apache ........................................................................................................................................................... 66 Michael Viogiatzis Blog ......................................................................................................................................... 66 Elasticsearch ............................................................................................................................................................. 67 Prediction IO ............................................................................................................................................................. 67 Container Cluster Manager ................................................................................................................................. 67 Domino Data Labs .................................................................................................................................................. 67 Data Science Central .............................................................................................................................................. 68 Amazon Web Services Videos ............................................................................................................................ 68 Google Cloud Computing Videos ...................................................................................................................... 68 VLAB: Deep Learning: Intelligence from Big Data, Stanford Graduate School of Business .... 68 Machine Learning and Big Data in Cyber Security Eyal Kolman Technion Lecture .................. 68 Chaire Machine Learning Big Data, Telecom Paris Tech (Videos in French) ................................ 68 An Architecture for Fast and General Data Processing on Large Clusters by Matei Zaharia, 2014 .............................................................................................................................................................................. 68

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V+%+$Y-&+3-+%-".$777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$?9 Visualization Lab Gallery, Computer Science Division, University of California, Berkeley .... 86 Visualization Lab Software, Computer Science Division, University of California, Berkeley 87 Visualization Lab Course Wiki, Computer Science Division, University of California, Berkeley ......................................................................................................................................................................................... 87 Mike Bostock ............................................................................................................................................................. 87 Eyeo Festival ............................................................................................................................................................. 87 MIT Data Collider .................................................................................................................................................... 87 D3 JS Data-Driven Documents ........................................................................................................................... 88 Shan He, Research Fellow at MIT Senseable City Lab ............................................................................. 88 Gource software version control visualization .......................................................................................... 88 Logstalgia, website access log visualization ................................................................................................ 88 Andrew Caudwell's Blog ...................................................................................................................................... 88

U""Z$=$>.13-)$7777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$?B An Architecture for Fast and General Data Processing on Large Clusters by Matei Zaharia, 2014 .............................................................................................................................................................................. 89 Deep Learning (Artificial Intelligence) , An MIT Press book in preparation, by Yoshua Bengio, Ian Goodfellow and Aaron Courville, 20-Oct-2014 ................................................................. 90 Deep Learning Tutorial by LISA Lab, University of Montreal, 2014 ................................................. 90 Statistical Inference for Everyone, by Professor Bryan Blais, 2014 ................................................. 91 Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman, 2014 ............... 91 Social Media Mining by Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, 2014 ........................ 92 Causal Inference by Miguel A. Hernn and James M. Robins, May 14, 2014, Draft .................... 93 Slides for High Performance Python tutorial at EuroSciPy2014 by Ian Ozsvald ........................ 93 Neural Networks and Deep Learning, 2014 ................................................................................................ 93 Probabilistic Programming and Bayesian Methods for Hackers by Cameron Davidson-Pilon, 2014 .............................................................................................................................................................................. 94 Bayesian Reasoning and Machine Learning, David Barber, 2012 (online version 02-2014) 94 Past, Present, and Future of Statistical Science by COPSS, 2014 ........................................................ 94 Essential of Metaheuristics by Sean Luke, 2013 ....................................................................................... 95 Statistical Model Building, Machine Learning, and the Ah-Ha Moment by Grace Wahba, 2013 .............................................................................................................................................................................. 95 An Introduction to Statistical Learning with applications in R. by Gareth James Daniela Witten Trevor Hastie Robert Tibshirani, 2013 (first printing) .......................................................... 95 A course in Machine Learning by Hal Daume, 2012 ................................................................................ 95 Machine Learning in Action, Peter Harrington, 2012 ............................................................................. 95 A Programmer's Guide to Data Mining, by Ron Zacharski, 2012 ....................................................... 95 Artificial Intelligence, Foundations of Computational Agents by David Poole and Alan Mackworth, 2010 .................................................................................................................................................... 96 The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman, 2009 ............. 96 Learning Deep Architecture for AI by Yoshua Bengio, 2009 ............................................................... 97 An Introduction to Information Retrieval by Christopher D. Manning Prabhakar Raghavan !"#$"%&(%&)*+,, 2009 ........................................................................................................................................... 97 Kernel Method in Machine Learning by Thomas Hofmann; Bernhard Schlkopf; Alexander J. Smola, 2008 ......................................................................................................................................................... 98 Introduction to Machine Learning, Alex Smola, S.V.N. Vishwanathan, 2008 ................................ 98 Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 .................................. 98 Gaussian processes for Machine Learning, C. Rasmussen and C. Williams, 2006 ...................... 99 Bayesian Machine Learning by Chakraborty, Sounak, 2005 .............................................................. 100


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Machine Learning by Tom Mitchell, 2005 .................................................................................................. 100 Information Theory, Inference, and Learning Algorithms, David McKay, 2003 ....................... 100 Free Book List ......................................................................................................................................................... 100 Free resource book (need to sign in) ........................................................................................................... 101 Free ML ebooks on it-ebooks, but this website is controversial, please read stackoverflow before accessing to this website by yourself ............................................................................................ 101 Wikipedia: Machine Learning, the Complete Guide ............................................................................... 101 ISSUU .......................................................................................................................................................................... 101

U""Z$@$2.13-)$777777777777777777777777777777777777777777777777$8[A Meetup's Presentations ...................................................................................................................................... 103 Slides .......................................................................................................................................................................... 103 Slideshare.com ....................................................................................................................................................... 103 Slides.com ................................................................................................................................................................ 103 Powershow.com .................................................................................................................................................... 103 Speaker Deck .......................................................................................................................................................... 103 Slides from Lectures ............................................................................................................................................ 104 Slides from Meetups ............................................................................................................................................ 104 Slides from Conferences ..................................................................................................................................... 104

;".R(0(.,( $7777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$8[N International Conference in Machine Learning (ICML) ....................................................................... 105 ICML, Beijing, China 2014 ................................................................................................................................. 105 ICML, Atlanta, US 2013 ...................................................................................................................................... 105 ICML, Edinburgh, UK 2012 ............................................................................................................................... 105 ICML, Bellevue, US 2011 .................................................................................................................................... 105 ICML, Haifa, Israel 2010 .................................................................................................................................... 105 Full archive of ICML ............................................................................................................................................ 105

Machine Learning Conference Videos ......................................................................................................... 105 Annual Machine Learning Symposium ........................................................................................................ 105 6th ............................................................................................................................................................................... 105 8th ................................................................................................................................................................................. 105


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Archive ...................................................................................................................................................................... 105 MLSS Machine Learning Summer Schools ................................................................................................. 106 Data Gotham 2012,2013 .................................................................................................................................... 106

*((%&.13-)$777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$8[T 631 Machine Learning Meetup in the World ............................................................................................ 107 Data Science Weekly List of Meetups ....................................................................................................... 107 Other Meetups missing in Data Science Weekly ..................................................................................... 107 London Machine Learning Meetup ............................................................................................................... 107 London Deep Learning Meetup ...................................................................................................................... 107

U3"1$=$>.13-)$77777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$8[? Data Science Weekly ............................................................................................................................................ 108 Yann LeCun, Google+ ........................................................................................................................................... 108 Igor Carron Blog .................................................................................................................................................... 108 KDD Community, Knowledge discovery and Data Mining .................................................................. 108 Kaggle Blog .............................................................................................................................................................. 108 Digg ............................................................................................................................................................................. 108 Feedly ......................................................................................................................................................................... 108 Mlwave ...................................................................................................................................................................... 108 FastML ....................................................................................................................................................................... 108 Beating the Benchmark ...................................................................................................................................... 109 YOU CANalytics ...................................................................................................................................................... 109 Trevor Stephens Blog .......................................................................................................................................... 109 Mozilla Hacks .......................................................................................................................................................... 109 Banach's Algorithmic Corner, University of Warsaw ............................................................................ 109 DataCamp Blog ....................................................................................................................................................... 110 Natural Language Processing Blog, Hal Daume ....................................................................................... 110 Maxim Milakov Blog ............................................................................................................................................ 110 Alfonso Nieto-Castanon Blog ........................................................................................................................... 110 Persontyle Blog ...................................................................................................................................................... 110 Analytics Vidhya .................................................................................................................................................... 110 Bugra Akyildiz's Blog .......................................................................................................................................... 111 Data origami ............................................................................................................................................................ 111 Rasbts Blog ............................................................................................................................................................. 111 Gilles Louppe's Blog ............................................................................................................................................. 111 AI Topics ................................................................................................................................................................... 111 AI International ..................................................................................................................................................... 112 Joseph Misiti's Blog .............................................................................................................................................. 112 MIRI, Machine Intelligence Research Institute ........................................................................................ 112 Kevin Davenport Data Blog .............................................................................................................................. 112 Alexandre Passant's Blog .................................................................................................................................. 113 Daniel Nouris Blog ............................................................................................................................................... 113 Yvonne Rogers Blog ............................................................................................................................................. 114

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U3"1$@$I+.13-)$77777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777777$88N Journal of Machine Learning Research, MIT Press ................................................................................. 115 Machine Learning Journal (last article could be downloaded for free) ........................................ 115 Machine Learning (Theory) ............................................................................................................................. 115 List of Journals on Microsoft Academic Research website ................................................................. 115 Wired magazine ..................................................................................................................................................... 115 Data Science Central ............................................................................................................................................ 115

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Zhihu.com ................................................................................................................................................................. 119 Machine Learning ................................................................................................................................................ 119 Data Mining ............................................................................................................................................................ 119 Artificial Intelligence .......................................................................................................................................... 119

Guokr.com ................................................................................................................................................................ 119 Machine Learning ................................................................................................................................................ 119 Data Mining ............................................................................................................................................................ 119 Artificial Intelligence .......................................................................................................................................... 119

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Oxford University .................................................................................................................................................. 133 Imperial College .................................................................................................................................................... 133 The University of Edinburgh, Institute for Adaptive and Neural Computation ........................ 134 Cambridge University ......................................................................................................................................... 134 Centre for Intelligent Sensing, Queen Mary University of London, UK .......................................... 134 ICRI, The Intel Collaborative Research Institute .................................................................................... 135

MACHINE LEARNING RESEARCH GROUPS in EUROPE, FRANCE .................................................... 135 Magnet, MAchine learninG in information NETworks, INRIA, France .......................................... 135 Sierra Team - Ecole Normale Superieure , CNRS, INRIA ..................................................................... 135 ENS Ecole Normale Superieure ...................................................................................................................... 136

MACHINE LEARNING RESEARCH GROUPS in EUROPE, GERMANY ............................................... 137 Max Planck Institute for Intelligent Systems, Tbingen site ............................................................. 137 BRML Research Lab, Institute of Informatics at the Technische Universitt Mnchen ....... 137

MACHINE LEARNING RESEARCH GROUPS in EUROPE, SWITZERLAND ..................................... 137 EPFL Ecole Polytechnique Federale de Lausanne, Switzerland ....................................................... 137 IDSIA: the Swiss AI Lab ...................................................................................................................................... 138

MACHINE LEARNING RESEARCH GROUPS in EUROPE, NETHERLANDS .................................... 138 Machine Learning Research Groups in The Netherlands .................................................................... 138

MACHINE LEARNING RESEARCH GROUPS in EUROPE, POLAND ................................................... 138 University of Warsaw, Dept. of Mathematics, Informatics and Mechanics ................................. 138

MACHINE LEARNING RESEARCH GROUPS in ASIA, INDIA ................................................................ 139 Indian Institute of Science ................................................................................................................................ 139 Indian Institute of Technology of Kanpur ................................................................................................. 139

MACHINE LEARNING RESEARCH GROUPS in ASIA, CHINA ............................................................... 139 Peking University .................................................................................................................................................. 139 Beijing University of Technology ................................................................................................................... 140 University of Science and Technology of China, USTC .......................................................................... 141 Nanjing University ............................................................................................................................................... 141

MACHINE LEARNING RESEARCH GROUPS in ASIA, RUSSIA ............................................................. 141 Moscow State University ................................................................................................................................... 141

MACHINE LEARNING RESEARCH GROUPS in AFRICA ......................................................................... 141 MACHINE LEARNING RESEARCH GROUPS in OCEANIA ..................................................................... 142 NICTA Machine Learning Research Group, Australia .......................................................................... 142

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University of Warsaw, POLAND ..................................................................................................................... 153 Marcin Murca ......................................................................................................................................................... 153

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a)+%$-$%)($*+,)-.($/(+0.-.1$2+3".4$5-%6$For now, the Machine Learning Salons Kit is just a collection of useful websites gathered on Blogs such as Datatau.com, Groups on LinkedIn, posts on Twitter, publications on Google Scholar, Universities websites, etc. We are just speaking French and English but we are gathering information from all over the World thanks to Google Translate! As an example, on MachineLearning.ru, from Russian translated in English, weve found a very helpful link to the annual report of the American Statistical Society. Who would expect that?

a)+%$-$."%$%)($*+,)-.($/(+0.-.1$2+3".4$5-%6$The Machine Learning Salons Kit is not a commercial product. We are not making any money of it. Its free, without any registration, and free from any advertising.

a)Q$+0($#($."%$".$C-%!&K6$We want to provide free worldwide information, and regarding the country, not everybody wants to register on GitHub (which is very helpful). We have found that the PDF file is the most universal solution.

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ER$Q"&$+0($#-33-.1$%"$1-X($+$P-,"&.%$%"$%)($*+,)-.($/(+0.-.1$2+3".4$0(+P(0$Just tell us what youre willing to propose and if we think that the readers will find it relevant, we will add it without any exchange of money. You will never get an email list of our visitors because we dont have any information about our visitors. We are working with a Basic Adobe Business Catalysts website, weve got the geographical location of our visitors, their loyalty, their page views, etc. but no IP addresses, We have nothing to sale and we are not willing to.

MK"&%$%)($F"&.P(0$"R$b)($*+,)-.($/(+0.-.1$2+3".4$a(K-%($L$5-%$ The Machine Learning Salon is founded by Jacqueline I. Forien who very much enjoyed her Master of Science in Machine Learning at University College London thanks to all her wonderful Machine Learning & Computational Statistics and Machine Learning's Peers and Teachers. Jacqueline would like to express a special gratitude to her director of Machine Learning studies at UCL, Professor Mark Herbster, her tutor, Professor David Barber, her supervisor of Master's project, Professor Nadia Berthouze. In addition, Jacqueline would like to express many thanks to Igor Carron who initiated the smart association of 'Machine Learning' and 'Salon', and gave her the opportunity to organise in London a wonderful event that was the Europe Wide Machine Learning Meetup between Paris, Berlin, Zurich and London with Andrew Ng as a Guest speaker.

;".%+,%$ Please, contact us if you want to add a contribution, remove a link, etc. Any suggestion is welcome! Contact at [email protected] $


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clustering and dimensionality reduction. Throughout the class there will be an emphasis not just on individual algorithms but on ideas that cut across them and tips for making them work. https://www.coursera.org/course/machlearning

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technologies are having a dramatic impact on the way people interact with computers, on the way people interact with each other through the use of language, and on the way people access the vast amount of linguistic data now in electronic form. From a scientific viewpoint, NLP involves fundamental questions of how to structure formal models (for example statistical models) of natural language phenomena, and of how to design algorithms that implement these models. https://www.coursera.org/course/nlangp

J0"K+K-3-%-,$C0+.1-.((0-.1$>X(0Q#)(0($SEE programming includes one of Stanfords most popular engineering sequences: the three-course Introduction to Computer Science taken by the majority of Stanford undergraduates, and seven more advanced courses in artificial intelligence and electrical engineering. Introduction to Computer Science Programming Methodology CS106A Programming Abstractions CS106B Programming Paradigms CS107 Artificial Intelligence Introduction to Robotics CS223A


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Natural Language Processing CS224N Machine Learning CS229 Linear Systems and Optimization The Fourier Transform and its Applications EE261 Introduction to Linear Dynamical Systems EE263 Convex Optimization I EE364A Convex Optimization II EE364B Additional School of Engineering Courses Programming Massively Parallel Processors CS193G iPhone Application Programming CS193P Seminars and Webinars http://see.stanford.edu/see/courses.aspx

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/(+0.-.1$R0"D$P+%+$O;+3%(,)S$This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. https://www.edx.org/course/caltechx/caltechx-cs1156x-learning-data-1120#.U5NNJxaRPwI https://www.edx.org/course/caltechx/caltechx-cs1156x-learning-data-1120#.U4oB75SSyG4

M0%-,-R-+3$E.%(33-1(.,($OU(0Z(3(QdS$CS188.1x is a new online adaptation of the first half of UC Berkeley's CS188: Introduction to Artificial Intelligence. The on-campus version of this upper division computer science course draws about 600 Berkeley students each year. Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on faces. AI lets you guide your phone with your voice and read foreign newspapers in English. Beyond today's applications, AI is at the core of many new technologies that will shape our future. From self-driving cars to household robots, advancements in AI help transform science fiction into real systems. CS188.1x focuses on Behavior from Computation. It will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific


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emphasis will be on the statistical and decisiontheoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in stochastic and in adversarial settings. CS188.2x (to follow CS188.1x, precise date to be determined) will cover Reasoning and Learning. With this additional machinery your agents will be able to draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in CS188x apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue. https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-cs188-1x-artificial-579#.U4CqKl6RPwI

U-1$V+%+$+.P$2",-+3$J)Q-,$O>%)-,S$Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack. https://www.edx.org/course/mitx/mitx-mas-s69x-big-data-social-physics-1737#.U4Cox5RdWG4

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VLAB is the San Francisco Bay Area chapter of the MIT Enterprise Forum, a non-profit organization dedicated to promoting the growth and success of high-tech entrepreneurial ventures by connecting ideas, technology and people. We provide a forum for San Francisco and Silicon Valley's leading entrepreneurs, industry experts, venture capitalists, private investors and technologists to exchange insights about how to effectively grow high-tech ventures amidst dynamic market risks and challenges. In a world where markets change at breakneck speed, knowledge is a critical source of competitive advantage. Our forums provide an excellent opportunity to network and learn about pivotal business issues, emerging industries and the latest technologies. http://www.youtube.com/user/vlabvideos/search?query=machine+learning

F"&.P+%-".$"R$*+,)-.($/(+0.-.1 $$KQ$*()0Q+0$*")0-$@$8[$Q(+0$"R$!"D(#"0Z$#-%)$2"3&%-".$+.P$/(,%&0($23-P(]$."%$%"$K($D-(P$e$Added the 11-Nov-2014 Course Description This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are: Probability tools, concentration inequalities PAC model Rademacher complexity, growth function, VC-dimension Perceptron, Winnow Support vector machines (SVMs) Kernel methods Decision trees Boosting Density estimation, maximum entropy models Logistic regression Regression problems and algorithms Ranking problems and algorithms Halving algorithm, weighted majority algorithm, mistake bounds Learning automata and transducers Reinforcement learning, Markov decision processes (MDPs) http://www.cs.nyu.edu/~mohri/ml14/

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take you directly to the video. The programs and public lectures are listed in reverse chronological order. Older videos play on Real Player only; recent videos will play on Flash supported browsers and software. https://www.ipam.ucla.edu/videos.aspx

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;+0.(1-($*(33".$.-X(0-%Q$O;*S$Y-P("$0("&0,( $"The videos below are intended to serve as resources for our current students, and not as online learning materials for students outside of our program." - The Machine Learning Department http://www.ml.cmu.edu/teaching/video-resources.html

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perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning. http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

Homework with solutions http://www.cs.cmu.edu/~tom/10701_sp11/hws.shtml

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We will be using Python for all programming assignments and projects. http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml

:fR"0P$.-X(0-%Q]$_+.P"$P($F0(-%+$X-P("$3(,%&0($I am a machine learning professor at UBC. I am making my lectures available to the world with the hope that this will give more folks out there the opportunity to learn some of the wonderful things I have been fortunate to learn myself. Enjoy. http://www.youtube.com/user/ProfNandoDF

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h+..$/(;&.4$J&K3-,+%-".$My main research interests are Machine Learning, Computer Vision, Mobile Robotics, and Computational Neuroscience. I am also interested in Data Compression, Digital Libraries, the Physics of Computation, and all the applications of machine learning (Vision, Speech, Language, Document understanding, Data Mining, Bioinformatics). http://yann.lecun.com/exdb/publis/index.html#fulllist

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b(,).-".]$E0+(3$E.%-%&%($"R$b(,)."3"1Q]$*+,)-.($/(+0.-.1$Y-P(" $Added the 22-Nov-2014 Technion - Israel Institute of Technology is Israel's biggest scientific-technological university and one of the largest centers of applied research in the world. Here the future is being shaped - by over 13,000 of Israel's most dynamic students active in 18 faculties. Technion is Israel's flagship of world-class education, bringing Israel its first Nobel Prizes in science. From the cornerstone laying ceremony in 1912, Technion's over 70,000 alumni have built the state of Israel and created and lead the majority of Israel's successful companies, impacting millions of scientists, students, entrepreneurs and citizens worldwide. http://www.youtube.com/user/Technion/search?query=machine+learning

_Jb>/]$_+%-".+3$J0"10+DD($".$b(,)."3"1Q$>.)+.,(P$/(+0.-.1]$E.P-+$NPTEL provides E-learning through online Web and Video courses in Engineering, Science and humanities streams. The mission of NPTEL is to enhance the quality of Engineering education in the country by providing free online courseware. http://nptel.ac.in

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*+f$J3+.,Z$E.%-%&%($R"0$E.%(33-1(.%$2Q%(D$b&K-.1(.]$*/22$Y-P("$\[8A $Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems. The Institute studies these principles in biological, computational, hybrid, and material systems ranging from nano to macro scales.We take a highly interdisciplinary approach that combines mathematics, computation, material science, and biology. The MPI for Intelligent Systems has campuses in Stuttgart and Tbingen. Our Stuttgart campus has world-leading expertise in small-scale intelligent systems that leverage novel material science and biology. The Tbingen campus focuses on how intelligent systems process information to perceive, act and learn. http://www.youtube.com/channel/UCty-pPOWlWUk4gXNm5pydcg

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*+,)-.($/(+0.-.1 $https://www.youtube.com/user/GoogleTechTalks/search?query=machine+learning

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G(-.R"0,(D(.%$/(+0.-.1$$O(3(,%$iY-(#$;"&0(#+0(i$R"0$R0(($+,,(S$Why Take This Course? You will learn about Reinforcement Learning, the field of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. Michael: Reinforcement Learning is a very popular field. Charles: Perhaps because you're in it, Michael. Michael: I don't think that's it. In this course, you will gain an understanding of topics and methods in Reinforcement Learning, including Markov Decision Processes and Game Theory. You will gain experience implementing Reinforcement Learning techniques in a final project. In the final project, well bring back the 80's and design a Pacman agent capable of eating all the food without getting eaten by monsters. https://www.udacity.com/course/ud820

*+%)(D+%-,+3D".Z$*+,)-.($/(+0.-.1 $Videos about math, at the graduate level or upper-level undergraduate. https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

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folks that will power innovation and disrupt industries, and ultimately shape our future. Hakka originally launched in SF Bay & NYC and rapidly built relationships with the top companies, CTOs and tech influencers in these key areas. We have deep connections to the software engineering worlds on both coasts and often invite groups of CTOs and engineers to our office in Soho, or meet with them at engineering events that we either run or participate in. We're also currently up & running in Berlin & Moscow, and plan to continue to rapidly expand worldwide. Not too shabby for a scrappy startup with a small marketing budget! http://www.hakkalabs.co https://www.youtube.com/user/g33ktalktv/videos

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Time series And more. . .

http://columbia-applied-data-science.github.io/appdatasci.pdf http://columbia-applied-data-science.github.io

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!".1$5".1$ :.13-)L;)-.((S$ $Wang Leung Wong The Vice-Chairperson of the Hong Kong Linux User Group This channel will post the videos of my life and opensource events in Hong Kong. Hong Kong Linux User Group: http://linux.org.hk Facebook: https://www.facebook.com/groups/hklug/ http://www.youtube.com/playlist?list=PL2FSfitY-hTKbEKNOwb-j0blK6qBauZ1f http://www.youtube.com/playlist?list=PL2FSfitY-hTLOL6tT_12YUK4c67e-E0xh

E;/G$\[8H$Y-P("$It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization. Despite the importance of representation learning to machine learning and to application areas such as vision, speech, audio and NLP, there is currently no common venue for researchers who share a common interest in this topic. The goal of ICLR is to help fill this void. ICLR 2014 will be a 3-day event from April 14th to April 16th 2014, in Banff, Canada. The conference will follow the recently introduced open reviewing and open publishing publication process, which is explained in further detail here: Publication Model. https://www.youtube.com/playlist?list=PLhiWXaTdsWB-3O19E0PSR0r9OseIylUM8

E;/G$\[8A$Y-P("$ICLR 2013 will be a 3-day event from May 2nd to May 4th 2013, co-located with AISTATS2013 in Scottsdale, Arizona. The conference will adopt a novel publication process, which is explained in further detail here: Publication Model. https://sites.google.com/site/representationlearning2013/program-details/program

*+,)-.($/(+0.-.1$;".R(0(.,($Y-P(" $Events matching your search: ICML 2011 Sixth Annual Machine Learning Symposium 1st Lisbon Machine Learning School Copulas in Machine Learning Workshop 2011 NIPS 2011 Workshop on Integrating Language and Vision Machine Learning in Computational Biology (MLCB) 2011


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Learning Semantics Workshop Sparse Representation and Low-rank Approximation The 4th International Workshop on Music and Machine

Learning: Learning from Musical Structure Big Learning: Algorithms, Systems, and Tools for Learning at

Scale ICML 2012 Oral Talks (International Conference on Machine

Learning) Big Data Meets Computer Vision: First International Workshop

on Large Scale Visual Recognition and Retrieval 2nd Workshop on Semantic Perception, Mapping and

Exploration (SPME) ICML 2012 Workshop on Representation Learning Inferning 2012: ICML Workshop on interaction between

Inference and Learning Object, functional and structured data: towards next

generation kernel-based methods - ICML 2012 Workshop Tutorial on Statistical Learning Theory in Reinforcement

Learning and Approximate Dynamic Programming Tutorial on Causal inference - conditional independences and

beyond ICML 2012 Tutorial on Prediction, Belief, and Markets PAC-Bayesian Analysis in Supervised, Unsupervised, and

Reinforcement Learning Performance Evaluation for Learning Algorithms: Techniques,

Application and Issues 2nd Lisbon Machine Learning School (2012) OpenCV using Python Big Learning : Algorithms, Systems, and Tools NIPS 2012 Workshop on Log-Linear Models Machine Learning in Computational Biology (MLCB) 2012 NYU Course on Big Data, Large Scale Machine Learning Sixteenth International Conference on Artificial Intelligence

and Statistics (AISTATS) 2013 International Conference on Learning Representations (ICLR)

2013 ICML 2013 Plenary Webcast


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NYU Course on Deep Learning (Spring 2014) NYU Course on Machine Learning and Computational Statistics

2014 http://techtalks.tv/search/results/?q=machine+learning

E.%(0.(%$M0,)-X($Hello Patron, Every day 3 million people use our collections. We have archived over ten petabytes (that's 10,000,000,000,000,000 bytes!) of information, including everything ever written in Balinese. This year we also launched our groundbreaking TV News Search and Borrow service, which former FCC Chairman Newton Minow said "offers citizens exceptional opportunities" to easily do their own fact checking and "to hold powerful public institutions accountable." Your support helps us build amazing services and keep them free for people around the globe. https://archive.org/search.php?query=machine%20learning

.-X(0-%Q$"R$U(0Z(3(Q$http://www.youtube.com/user/UCBerkeley/search?query=machine+learning

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Syntax: TurboParser, an open-source, trainable statistical dependency parser; MSTParserStacked, an open-source, trainable statistical dependency parser based on stacking; DAGEEM code for unsupervised dependency grammar induction Information extraction: Arabic named entity recognizer Libraries/languages: AD3, an approximate MAP decoder; *Dyna, a declarative programming language for dynamic programming algorithms Machine translation tools, including: *cdec, a framework for statistical translation and other structure prediction problems; *Egypt, a statistical machine translation toolkit that includes Giza; gappy pattern models, code for modeling monolingual and bilingual textual patterns with gaps; Rampion, a training algorithm for statistical machine translation models Social media tools, including: Twitter NLP resources Datasets: *STRAND (parallel text collections from the web); CURD (the Carnegie Mellon University Recipe Database); 10-K Corpus (company annual reports and stock return volatility data); political blog corpus; movie$ corpus; movie summary corpus; question-answer data; Congressional bills corpus; Arabic named entity and supersense corpora; NFL tweets corpus; multiword expressions corpus Project websites: Flexible Learning for NLP; Low-Density MT; Compuframes, Big Multilinguality, Corporate Social Network http://www.ark.cs.cmu.edu/#resources

>2M;$VMbM$M_M/h2E2$M_V$2bMbE2bE;2$a:G52!:J$\[8H$ABOUT THE ESAC FACULTY The ESAC Faculty was created in 2006 in order to foster an effective scientific environment at ESAC, and to to present a united face to the scientific work done at the centre. The faculty includes all active (i.e. publishing papers) research scientists at ESAC: ESA staff, Research Fellows, Science Contractors, and LAEFF members. For an insight into the founding principles, see the Overview of the ESAC Faculty presentation given at the first assembly. The ESAC Faculty's main purpose is to stimulate and promote science activities at ESAC. For this it maintains an active and attractive visitor programme for short-to-medium term collaborative stays at ESAC, covering established researchers as well as young post-docs, PhD and graduate students. The Faculty also supports visiting seminar speakers, conferences, workshops and travel not possibly via normal mission budgets. ESAC Faculty members pursue their own research (as per the scientific interests of individual members), but are also involved in numerous internal and external collaborations (overview of Faculty Science at ESAC). Faculty members are also strongly involved in the ESAC Trainee programme. http://www.cosmos.esa.int/web/esac-science-faculty/esac-statistics-workshop-2014

b)($G"Q+3$2",-(%Q$$The Royal Society is a self-governing Fellowship of many of the worlds most distinguished scientists drawn from all areas of science, engineering, and medicine.


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The Societys fundamental purpose, reflected in its founding Charters of the 1660s, is to recognise, promote, and support excellence in science and to encourage the development and use of science for the benefit of humanity. The Society has played a part in some of the most fundamental, significant, and life-changing discoveries in scientific history and Royal Society scientists continue to make outstanding contributions to science in many research areas. The Royal Society is the national Academy of science in the UK, and its core is its Fellowship and Foreign Membership, supported by a dedicated staff in London and elsewhere. The Fellowship comprises the most eminent scientists of the UK, Ireland and the Commonwealth. A major activity of the Society is identifying and supporting the work of outstanding scientists. The Society supports researchers through its early and senior career schemes, innovation and industry schemes, and other schemes. The Society facilitates interaction and communication among scientists via its discussion meetings, and disseminates scientific advances through its journals. The Society also engages beyond the research community, through independent policy work, the promotion of high quality science education, and communication with the public. https://www.youtube.com/user/RoyalSociety/videos?spfreload=10

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b)($#".P(0R&3$+.P$%(00-RQ-.1$-DVfU0&(3$Published on 6 Dec 2014 This talk was given at a local TEDx event, produced independently of the TED Conferences. The extraordinary, wonderful, and terrifying implications of computers that can learn https://www.youtube.com/watch?v=xx310zM3tLs&spfreload=10

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fractionnaires des sommets ou des artes, graphes de Kneiser), les problmes de transversales d'un graphe (parcours eulriens, cycles hamiltoniens, graphes de DeBruijn, etc.) et la notion de marche alatoire sur un graphe (chanes de Markov, existence de la distribution limite, mixing time, etc.). Plusieurs problmes sur les graphes ont d'lgantes solutions, d'autres videmment sont NP-complets; une partie de ce cours portera donc sur la thorie de la complexit (problmes NP et NP-complets, thorme de Cook, algorithmes de rductions). https://cours.ift.ulaval.ca/2012a/ift7012_89927/

!&1" $/+0",)(33(]$Mf(0,-($#-%)$"3&%-".$O>.13-)@F0(.,)S$$Spring 2014: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay) Fall 2013: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Spring 2013: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay) Spring 2013: Statistical machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure (Paris) Fall 2012: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Spring 2012: Statistical machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure (Paris) Spring 2012: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay) Fall 2011: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Spring 2011: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)


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Fall 2010: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Spring 2010: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay) Fall 2009: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Fall 2008: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan May 2008: Probabilistic modelling and graphical models: Enseignement Specialise - Ecole des Mines de Paris Fall 2007: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan May 2007: Probabilistic modelling and graphical models: Enseignement Specialise - Ecole des Mines de Paris Fall 2006: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan Fall 2005: An introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de Cachan http://www.di.ens.fr/~fbach/

;"33(1($P($F0+.,(]$*+%)(D+%-,$+.P$V-1-%+3$2,-(.,(]$F0(.,)$One of the Collge de France's missions is to promote French research and thought abroad, and to participate in intel-lectual debates on major world issues. The institution therefore participates in international exchange through its teaching and the dissemination of knowledge, as well as through the research programmes involving its Chairs and laboratories. The fact that one fifth of the professors are currently from abroad, confirms the Collge de France's wid-ening research and education policy. This policy of international openness translates into: Collge de France professors' teaching missions abroad Lectures and lecture series by visiting professors Junior Visiting Researchers scheme Lecture series and symposia abroad Internet broadcasts http://www.college-de-france.fr/site/audio-video/_audiovideos.jsp?index=0&prompt=&fulltextdefault=mots-cles...&fulltext=&fields=TYPE2_ACTIVITY&fieldsdefault=0_0&TYPE2=0&ACTIVITY=mathematiques more to come

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Classification Pattern recognition Regression analysis Analysis and understanding of images Prediction Processing and analysis of texts Applied Statistics Applied Systems Analysis Data Signal Processing All Destinations

http://www.machinelearning.ru/wiki/index.php?title=_

h+.P(f$2,)""3 The Yandex School of Data Analysis$The School of Data Analysis is a free Masters-level program in Computer Science and Data Analysis, which is offered by Yandex since 2007 to graduates in engineering, mathematics, computer science or related fields. The aim of the School is to train specialists in data analysis and information retrieval for further employment at Yandex or any other IT company. The Schools courses are taught by Russian and international experts at Yandexs Moscow office in the evenings, several times a week. The average study load is 15-20 hours per week, including 9-12 hours of lectures and seminars. The School also runs distance-learning courses and provides lectures over the internet. All courses at the Yandex School of Data Analysis are currently taught only in Russian. http://shad.yandex.ru/lectures/

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E.%0"P&,%-".$%"$V+%+$*-.-.1$O\[8\S$ http://alexanderdyakonov.narod.ru/intro2datamining.pdf

b0-,Z$-.$V+%+$*-.-.1$O\[88S$ http://alexanderdyakonov.narod.ru/lpotdyakonov.pdf

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*+,)-.($/(+0.-.1$3(,%&0($KQ$Konstantin Vorontsov.$http://shad.yandex.ru/lectures/machine_learning.xml More to come

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M0%-R-,-+3$E.%(33-1(.,($http://mooc.guokr.com/search/?wd=%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD More coming soon

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mostly because of the complexity of dealing with emotional and motivational aspects of self-directed activity increase. By providing the means to automatically recognise, interpret, and act upon human affective states, recent developments in sensing technology and the field of affective computing offer new avenues for addressing these limitations and alleviating the difficulties patients face in building on treatment gains. Thus we propose the design and development of an intelligent system that will enable ubiquitous monitoring and assessment of patients pain-related mood and movements inside (and in the longer term, outside) the clinical environment. Specifically, we aim to (a) develop a set of methods for automatically recognising audiovisual cues related to pain, behavioural patterns typical of low back pain, and affective states influencing pain, and (b) integrate these methods into a system that will provide appropriate feedback and prompts to the patient based on his/her behaviour measured during self-directed physical therapy sessions. In doing so, we seek to develop a new generation of multimodal patient-centred personal health technology. http://www.emo-pain.ac.uk _!5$V",&D(.%+0Q$sG"K"%$G(X"3&%-".t$V(X(3"


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irrelevant search results you dont see, just to name a new. Machine learning is one of the best technologies we have for solving some of the biggest problems on the Web. http://labs.yahoo.com/areas/?areas=machine-learning

*-,0""R%$G((+0,)$The Machine Learning Groups of Microsoft Research include a set of researchers and developers who push the state of the art in machine learning. We span the space from proving theorems about the math underlying ML, to creating new ML systems and algorithms, to helping our partner product groups apply ML to large and complex data sets. http://research.microsoft.com/en-us/groups/mldept/

I"&0.+3$R0"D$*Eb$J0($The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. http://jmlr.org

E_GEM$Access to Research Papers http://haltools.inrialpes.fr/Public/afficheRequetePubli.php?labos_exp=sierra&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Anglais&tri_exp=annee_publi&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuCondense.css

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a(Z+$Au$V+%+$*-.-.1$2"R%#+0($-.$I+X+$Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. http://www.cs.waikato.ac.nz/~ml/weka/index.html

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http://nbviewer.ipython.org/github/craffel/theano-tutorial/blob/master/Theano%20Tutorial.ipynb

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sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. http://www.numpy.org

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JQ*; $PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. http://pymc-devs.github.io/pymc/

JQ3(+0.\$Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frdric Bastien, and Yoshua Bengio. "Pylearn2: a machine learning research library". arXiv preprint arXiv:1308.4214 (BibTeX) https://github.com/lisa-lab/pylearn2

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http://www.youtube.com/user/MontrealPython/videos http://montrealpython.org/en/

2,-JQ$\[8H$SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference allows participants from all types of organizations to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. http://pyvideo.org/category/51/scipy-2014

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also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. http://www.gnu.org/software/octave/

I/EM$Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed par