20160525 跨界新識力沙龍論壇 機器學習與跨業應用展望

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  • (Raymond)

    https://blogs.msdn.microsoft.com/mengtsai/

    https://blogs.msdn.microsoft.com/mengtsai/

  • 1999 M.S. Degree in Computer Science US. Interactive Inc. Sierra Systems

    2005 EMBA (104)

    Microsoft Azure Windows AppsKinectWindows 10Azure

  • Agenda

    ?

  • 2000

  • 2000~2005

    DATACENTERS (CLOUD)

    PC / DEVICE

    DIGITAL TAPE

    DVD / BLU-RAY

    CD

  • 40%

    CLOUD / IoT

    PC / MOBILE

  • 10

    CLOUD

    MOBILE

  • Machine Learning?

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem.

    Introduction to Machine Learning, 2nd Edition, MIT Press

    A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E

    - Tom M. Mitchell, Chair of the Machine Learning Department at CMU

  • ?

    18

  • ?

    Azure ML Machine Learning

    19

  • ?

    :

    Classification ()

    Regression ()

    Clustering ()

    Density estimation

    Dimensionality Reduction

    (2-class)

    (3)

    ()

    http://en.wikipedia.org/wiki/Machine_learning

    http://en.wikipedia.org/wiki/Machine_learning

  • Machine Learning ?

    21

  • 22

    ()

    https://www.youtube.com/watch?v=zOPIvC0MlA4#t=45

    https://www.youtube.com/watch?v=zOPIvC0MlA4#t=45https://www.youtube.com/watch?v=zOPIvC0MlA4#t=45https://www.youtube.com/watch?v=zOPIvC0MlA4#t=45

  • https://www.youtube.com/watch?v=R2mC-NUAmMk

    https://www.youtube.com/watch?v=R2mC-NUAmMkhttps://www.youtube.com/watch?v=R2mC-NUAmMkhttps://www.youtube.com/watch?v=R2mC-NUAmMk

  • 27

  • &

    &

    28

    http://www.wsj.com/articles/is-the-tech-bubble-popping-ping-pong-offers-an-answer-1462286089

    http://www.wsj.com/articles/is-the-tech-bubble-popping-ping-pong-offers-an-answer-1462286089

  • vs.

  • ?http://demos.datasciencedojo.com/demo/titanic/

    http://demos.datasciencedojo.com/demo/titanic/http://demos.datasciencedojo.com/demo/titanic/http://demos.datasciencedojo.com/demo/titanic/

  • 1.

    2.

    3.

  • https://studio.azureml.net/community/unpack?packageUri=https%3a%2f%2fstorage.azureml.net%2fdirectories%2fc8c02ed8f99841388a460a1df0a1b6d2&communityUri=https%3a%2f%2fgallery.azureml.net%2fDetails%2f01b2765fa75147ce99679e18482d280f

    https://studio.azureml.net/community/unpack?packageUri=https://storage.azureml.net/directories/c8c02ed8f99841388a460a1df0a1b6d2&communityUri=https://gallery.azureml.net/Details/01b2765fa75147ce99679e18482d280f

  • https://blogs.msdn.microsoft.com/mengtsai/2015/01/05/azure/

    https://blogs.msdn.microsoft.com/mengtsai/2015/01/05/azure/https://blogs.msdn.microsoft.com/mengtsai/2015/01/05/azure/https://blogs.msdn.microsoft.com/mengtsai/2015/01/05/azure/

  • Descriptive () What happened?

    Diagnostic () Why did it happen?

    Predictive () What will happen?

    Prescriptive () What should I do?

    35

  • Machine Learning ?

    Machine Learning

    36

    - ()- (/)- ()-()

  • in Finance

    39

    Financial Markets & etc. Retail Banking Insurance

    /Duration

    Market

    Assets Price

    Prediction

    Social

    Network

    Analysis

    Fraud

    Detection

    Risk Analysis

    Compliance

    &

    Regulatory

    Reporting

    Advertising

    Campaign

    Optimizatio

    n

    News

    Analysis

    Customer

    Loyalty &

    Marketing

    Improving

    operational

    efficiencies

    Credit

    Scoring

    Brand

    Sentiment

    Analysis

    Personalize

    d Product

    Offering

    Customer

    Segmentation

    Reference: http://0xcode.in/big-data-in-banking

    http://0xcode.in/big-data-in-banking

  • in Finance-

    40

    C (Classification )CA (Cluster Analysis )LSA (Latent Semantic Analysis )AD (Anomaly Detection )CF (Collaborative Filtering /)

  • (Credit Scoring)

    ?

    1,000 (UC Irvine ) 22

    41

  • (Credit Scoring)

    SVM 72.6%

    42

  • 43

  • 45

  • vs. Deep Learning vs. Machine Learning

    1.

    2.

    Deep learning carries out the machine learning process using an artificial

    neural net that is composed of a number of levels arranged in a hierarchy.

  • 47

    *2 w/ 200; 95% => 98.1% *4 w/ 200; 98.1% => 99.7%

  • AlphaGo (Deep Learning)

    http://technews.tw/2016/01/30/google-alphago-2/

    http://googleresearch.blogspot.tw/2016/01/alphago-mastering-

    ancient-game-of-go.html

  • Decision Tree

    Decision Jungle

    Decision Forest

  • K-means

    51

    (k=3)

    Step 1. 3 :

  • K-means

    52

    Step 2. 3

  • K-means

    53

    Step 3. 3

    Step 2 & 3~