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Brief introduction to Machine Learning

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Talk by Michael Krech (@Parsec) held during the Open Data Meetup in Frankfurt: http://www.meetup.com/de/OKLabFfm/events/219667158/

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Brief Introduction to Machine Learning Michael Krech, @Parsec
@Parsec A brief introduction to Machine Learning OUR MISSION Chapter 1: History - Present Chapter 2: Overview Chapter 3: Computer can .. Chapter 4: Why now? Chapter 5: Future
@Parsec A brief introduction to Machine Learning CHAPTE R 1
@Parsec A brief introduction to Machine Learning HISTORY
@Parsec A brief introduction to Machine Learning PRESENT
@Parsec A brief introduction to Machine Learning PRESENT
@Parsec A brief introduction to Machine Learning PRESENT What are my recommended movies ?
@Parsec A brief introduction to Machine Learning PRESENT What are my recommended friends?
@Parsec A brief introduction to Machine Learning PRESENT https://www.youtube.com/watch?v=YgYSv2KSyWg In initial tests run during 2006 by David Ferrucci, the senior manager of IBM's Semantic Analysis and Integration department,Watson was given 500 clues from past Jeopardy! programs. While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues,Watson's first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. By 2008, the developers had advanced Watson such that it could compete with Jeopardy! Champions. By February 2010,Watson could beat human Jeopardy! contestants on a regular basis
@Parsec A brief introduction to Machine Learning PRESENT In June 2015, the team announced that their vehicles have now driven over 1 million miles, stating that this was "the equivalent of 75 years of typical U.S. adult driving", and that in the process they had encountered 200,000 stop signs, 600,000 traffic lights, and 180 million other vehicles. Google also announced its prototype vehicles were being road tested in MountainView, California. During testing, the prototypes' speed cannot exceed 25 mph and will have safety drivers aboard the entire time.
@Parsec A brief introduction to Machine Learning CHAPTE R 2
@Parsec A brief introduction to Machine Learning OVERVIEW
@Parsec A brief introduction to Machine Learning 2,5 KINDS OF MACHINE LEARNING ALGORITHMS   Supervised Learning Unsupervised Learning   Reinforcement learning
@Parsec A brief introduction to Machine Learning SUPERVISED-LEARNING Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way
@Parsec A brief introduction to Machine Learning UNSUPERVISED LEARNING In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning. Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Many methods employed in unsupervised learning are based on data mining methods used to preprocess[citation needed] data. e.g.: Anomaly detection …
@Parsec A brief introduction to Machine Learning REINFORMENT-LEARNING, (SEMI-SUPERVISED) Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. This is the most common form, humans learn.
@Parsec A brief introduction to Machine Learning REINFORMENT-LEARNING, (SEMI-SUPERVISED) http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
@Parsec A brief introduction to Machine Learning WORKSPACES   Reporting   Search   Exploration   Prediction   Classification
@Parsec A brief introduction to Machine Learning CHAPTE R 3
@Parsec A brief introduction to Machine Learning COMPUTER CAN …. ?
@Parsec A brief introduction to Machine Learning LISTEN, SPEAK https://www.youtube.com/watch?v=Nu-nlQqFCKg Speech Recognition Breakthrough for the Spoken, Translated Word
@Parsec A brief introduction to Machine Learning SEE http://benchmark.ini.rub.de/?section=gtsrb&subsection=results Computer better as humans
@Parsec A brief introduction to Machine Learning RECOGNITION Google can identify and transcribe all the views it has of street numbers in France in less than an hour, thanks to a neural network that’s just as good as human operators. http://www.technologyreview.com/view/523326/how-google-cracked-house- number-identification-in-street-view/ Wie lange und wie viele Menschen hätte dies benötigt?
@Parsec A brief introduction to Machine Learning RECOGNITON AND CLUSTER A selection of evaluation results, grouped by human rating. http://googleresearch.blogspot.in/2014/11/a-picture-is-worth-thousand- coherent.html
@Parsec A brief introduction to Machine Learning SING https://www.youtube.com/watch?v=dKUDHPw15m0
@Parsec A brief introduction to Machine Learning READ OCR http://de.wikipedia.org/wiki/Texterkennung
@Parsec A brief introduction to Machine Learning UNDERSTAND http://nlp.stanford.edu/sentiment/ http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf
@Parsec A brief introduction to Machine Learning COMPUTER CAN … Read & Write Listen, Speak Singen See, Recognition Understand
@Parsec A brief introduction to Machine Learning CHAPTE R 4
@Parsec A brief introduction to Machine Learning WHY THIS WORKS BETTER NOW CLOUD / CLUSTER / CORES TOOLS Technologies (Forbes) GPU, SaaS, PaaS, IaaS, Amazon Cloud Computing, Google Cloud Computing …. Hadoop, Spark, Elasticsearch, MongoDB, Neo4j, Mahout, Python, R Deep Learning, Clustering, Anomaly Detection, ConvNet, Reinforcement, Statistics
@Parsec A brief introduction to Machine Learning BIOLOGIC PYRAMID @mike_schatz
@Parsec A brief introduction to Machine Learning CHAPTE R 5
@Parsec A brief introduction to Machine Learning Moores Law, complexity of IC’s double’s in 12-24 Month
@Parsec A brief introduction to Machine Learning T-1000 http://en.wikipedia.org/wiki/T-1000
@Parsec A brief introduction to Machine Learning TURING TEST Back in 2002 Kurzweil (a scientist renowned for his accurate tech predictions), bet Mitch Kapor (founder of Lotus Development Corp., inventor of spreadsheet software) $20,000 that a computer would pass the Turing Test by 2029. He predicts a Singularity for the yeaar 2045…. Er prognostiziert für das Jahr 2045 eine exponentielle Zunahme der informationstechnologischen Entwicklung: Eine Singularität, die eine künstliche Intelligenz ermöglicht, mit welcher die Menschheit Unsterblichkeit erlangen kann. https://en.wikipedia.org/wiki/ Predictions_made_by_Ray_Kurzweil#204 5:_The_Singularity (Wikipedia) Alan Turing Ray Kurzweil Raymond "Ray" Kurzweil is an American author, computer scientist, inventor, futurist, and is a director of engineering at Google. Aside from futurology, he is involved in fields such as optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments. He has written books on health, artificial intelligence (AI), transhumanism, the technological singularity, and futurism. Kurzweil is a public advocate for the futurist and transhumanist movements, as has been displayed in his vast collection of public talks, wherein he has shared his primarily optimistic outlooks on life extension technologies and the future of nanotechnology, robotics, and biotechnology. The Turing test was introduced by Alan Turing in 1950. The Turing test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
THANK YOU

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Brief introduction to Machine Learning

  • 1. Brief Introduction to Machine Learning Michael Krech, @Parsec
  • 2. @Parsec A brief introduction to Machine Learning OUR MISSION Chapter 1: History - Present Chapter 2: Overview Chapter 3: Computer can .. Chapter 4: Why now? Chapter 5: Future
  • 3. @Parsec A brief introduction to Machine Learning CHAPTE R 1
  • 4. @Parsec A brief introduction to Machine Learning HISTORY
  • 5. @Parsec A brief introduction to Machine Learning PRESENT
  • 6. @Parsec A brief introduction to Machine Learning PRESENT
  • 7. @Parsec A brief introduction to Machine Learning PRESENT What are my recommended movies ?
  • 8. @Parsec A brief introduction to Machine Learning PRESENT What are my recommended friends?
  • 9. @Parsec A brief introduction to Machine Learning PRESENT https://www.youtube.com/watch?v=YgYSv2KSyWg In initial tests run during 2006 by David Ferrucci, the senior manager of IBM's Semantic Analysis and Integration department,Watson was given 500 clues from past Jeopardy! programs. While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues,Watson's first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. By 2008, the developers had advanced Watson such that it could compete with Jeopardy! Champions. By February 2010,Watson could beat human Jeopardy! contestants on a regular basis
  • 10. @Parsec A brief introduction to Machine Learning PRESENT In June 2015, the team announced that their vehicles have now driven over 1 million miles, stating that this was "the equivalent of 75 years of typical U.S. adult driving", and that in the process they had encountered 200,000 stop signs, 600,000 traffic lights, and 180 million other vehicles. Google also announced its prototype vehicles were being road tested in MountainView, California. During testing, the prototypes' speed cannot exceed 25 mph and will have safety drivers aboard the entire time.
  • 11. @Parsec A brief introduction to Machine Learning CHAPTE R 2
  • 12. @Parsec A brief introduction to Machine Learning OVERVIEW
  • 13. @Parsec A brief introduction to Machine Learning 2,5 KINDS OF MACHINE LEARNING ALGORITHMS   Supervised Learning Unsupervised Learning   Reinforcement learning
  • 14. @Parsec A brief introduction to Machine Learning SUPERVISED-LEARNING Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way
  • 15. @Parsec A brief introduction to Machine Learning UNSUPERVISED LEARNING In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning. Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Many methods employed in unsupervised learning are based on data mining methods used to preprocess[citation needed] data. e.g.: Anomaly detection …
  • 16. @Parsec A brief introduction to Machine Learning REINFORMENT-LEARNING, (SEMI-SUPERVISED) Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. This is the most common form, humans learn.
  • 17. @Parsec A brief introduction to Machine Learning REINFORMENT-LEARNING, (SEMI-SUPERVISED) http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
  • 18. @Parsec A brief introduction to Machine Learning WORKSPACES   Reporting   Search   Exploration   Prediction   Classification
  • 19. @Parsec A brief introduction to Machine Learning CHAPTE R 3
  • 20. @Parsec A brief introduction to Machine Learning COMPUTER CAN …. ?
  • 21. @Parsec A brief introduction to Machine Learning LISTEN, SPEAK https://www.youtube.com/watch?v=Nu-nlQqFCKg Speech Recognition Breakthrough for the Spoken, Translated Word
  • 22. @Parsec A brief introduction to Machine Learning SEE http://benchmark.ini.rub.de/?section=gtsrb&subsection=results Computer better as humans
  • 23. @Parsec A brief introduction to Machine Learning RECOGNITION Google can identify and transcribe all the views it has of street numbers in France in less than an hour, thanks to a neural network that’s just as good as human operators. http://www.technologyreview.com/view/523326/how-google-cracked-house- number-identification-in-street-view/ Wie lange und wie viele Menschen hätte dies benötigt?
  • 24. @Parsec A brief introduction to Machine Learning RECOGNITON AND CLUSTER A selection of evaluation results, grouped by human rating. http://googleresearch.blogspot.in/2014/11/a-picture-is-worth-thousand- coherent.html
  • 25. @Parsec A brief introduction to Machine Learning SING https://www.youtube.com/watch?v=dKUDHPw15m0
  • 26. @Parsec A brief introduction to Machine Learning READ OCR http://de.wikipedia.org/wiki/Texterkennung
  • 27. @Parsec A brief introduction to Machine Learning UNDERSTAND http://nlp.stanford.edu/sentiment/ http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf
  • 28. @Parsec A brief introduction to Machine Learning COMPUTER CAN … Read & Write Listen, Speak Singen See, Recognition Understand
  • 29. @Parsec A brief introduction to Machine Learning CHAPTE R 4
  • 30. @Parsec A brief introduction to Machine Learning WHY THIS WORKS BETTER NOW CLOUD / CLUSTER / CORES TOOLS Technologies (Forbes) GPU, SaaS, PaaS, IaaS, Amazon Cloud Computing, Google Cloud Computing …. Hadoop, Spark, Elasticsearch, MongoDB, Neo4j, Mahout, Python, R Deep Learning, Clustering, Anomaly Detection, ConvNet, Reinforcement, Statistics
  • 31. @Parsec A brief introduction to Machine Learning BIOLOGIC PYRAMID @mike_schatz
  • 32. @Parsec A brief introduction to Machine Learning CHAPTE R 5
  • 33. @Parsec A brief introduction to Machine Learning Moores Law, complexity of IC’s double’s in 12-24 Month
  • 34. @Parsec A brief introduction to Machine Learning T-1000 http://en.wikipedia.org/wiki/T-1000
  • 35. @Parsec A brief introduction to Machine Learning TURING TEST Back in 2002 Kurzweil (a scientist renowned for his accurate tech predictions), bet Mitch Kapor (founder of Lotus Development Corp., inventor of spreadsheet software) $20,000 that a computer would pass the Turing Test by 2029. He predicts a Singularity for the yeaar 2045…. Er prognostiziert für das Jahr 2045 eine exponentielle Zunahme der informationstechnologischen Entwicklung: Eine Singularität, die eine künstliche Intelligenz ermöglicht, mit welcher die Menschheit Unsterblichkeit erlangen kann. https://en.wikipedia.org/wiki/ Predictions_made_by_Ray_Kurzweil#204 5:_The_Singularity (Wikipedia) Alan Turing Ray Kurzweil Raymond "Ray" Kurzweil is an American author, computer scientist, inventor, futurist, and is a director of engineering at Google. Aside from futurology, he is involved in fields such as optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments. He has written books on health, artificial intelligence (AI), transhumanism, the technological singularity, and futurism. Kurzweil is a public advocate for the futurist and transhumanist movements, as has been displayed in his vast collection of public talks, wherein he has shared his primarily optimistic outlooks on life extension technologies and the future of nanotechnology, robotics, and biotechnology. The Turing test was introduced by Alan Turing in 1950. The Turing test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.