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    )01-805-11-13(

    http://faculties.sbu.ac.ir/~a_mahmoudi/

    Machine Learning

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    .:ML94))subject

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    TAs

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    [email protected]

    http://faculties.sbu.ac.ir/~a_mahmoudi/ML_94_1.htm

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    Introduction to Machine Learning,

    Third EditionEthem Alpaydin

    Machine Learning: A Probabilistic

    PrespectiveKevin Murphy

    Pattern RecognitionTheodoridis & Koutroumbas

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    5

    Pattern Recognition and Machine Learning

    Christopher Bishop

    Pattern classificationRichard O. Duda, Peter E. Hart and David G. Stork

    Machine Learning

    Tom Mitchell

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    25-2030-15

    60-50

    5%

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    ))Matlab

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    1 Introduction2 Supervised learning

    3 Bayesian Decision Theory

    4 Parametric Methods

    5 Multivariate Methods

    6 Dimensionality Reduction

    7 Nonparametric method

    8 Decision Tree9 Linear Discrimination

    10 Support Vector Machine

    11 Neural Networks

    12 Hidden Markov Model

    13 Assessing Classification Algorithm

    14 Combining Multiple Learner

    15 Reinforcement Learning

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    UCIRepository:

    http://www.ics.uci.edu/~mlearn/MLRepository.html

    UCIKDDArchive:

    http://kdd.ics.uci.edu/summary.data.application.html

    Statlib:http://lib.stat.cmu.edu/

    Delve:http://www.cs.utoronto.ca/~delve/

    9

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    Journalof

    Machine

    Learning

    Research

    www.jmlr.org

    MachineLearning

    NeuralComputation

    NeuralNetworks

    IEEETransactionsonNeuralNetworks

    IEEE

    Transactions

    on

    Pattern

    Analysis

    and

    Machine

    Intelligence

    AnnalsofStatistics

    Journal

    of

    the

    American

    Statistical

    Association PatternRecognition

    Nature

    10

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    InternationalConference

    on

    Machine

    Learning

    (ICML) EuropeanConferenceonMachineLearning(ECML) NeuralInformationProcessingSystems(NIPS) Uncertainty

    in

    Artificial

    Intelligence

    (UAI)

    ComputationalLearningTheory(COLT) InternationalConferenceonArtificialNeural

    Networks(ICANN)

    InternationalConferenceonAI&Statistics(AISTATS)

    InternationalConferenceonPatternRecognition

    (ICPR) ...

    11

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    Ethem Alpayd in

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    :

    )BSS(

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    Learning is the act of acquiring new, or modifying andreinforcing existing knowledge, behaviors, skills,

    values, or preferences.

    The ability to learn is possessed by humans, animalsand some machines.

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    .).)Tom.M.Mitchell

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    Machine learning is programming computers tooptimize a performance criterion using example dataor past experience.

    Machine Learning

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    Field of study that gives computers the ability

    to learn without being explicitly programmed.

    Well-posed Learning Problem: A computer program is said to learn from

    experience E with respect to some task T and some performancemeasure P, if its performance on T, as measured by P, improves with

    experience E.

    Machine Learning

    Arthur Samuel (1959)

    Tom Mitchell (1998)

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    spam

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    Tspam/not spam

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    spam

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    USC CS Distinguished Lecture Series, 2008

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    We are drowning in information and starving for

    knowledge. John Naisbitt.

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    )

    (...

    :

    20

    )(

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    (...

    :

    online

    .

    .

    ))predictive

    .))descriptive

    .

    21

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    .

    ))CRM:

    :

    :

    :

    ))intrusion detection:

    )

    (

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    Data Mining

    Knowledge Discovery in Database (KDD)

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    :

    X .Y

    P

    (Y

    |X)

    P(chips|beer)=0.7

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    Learning Associations

    Association Rule

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    )creditscoring(

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    Classifications

    Discriminant:IFincome>1ANDsavings>2

    THENlowrisk

    ELSEhigh

    risk

    Discriminant

    )(

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    )

    (...

    .

    ))OCR

    t?e

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    )(

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    (...

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    Training examples of a person

    Test images

    ORL dataset,AT&T Laboratories, Cambridge UK

    )(

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    (...

    Sensorfusion

    ))outlierdetection

    IntrusionDetectionSystems

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    )supervised(

    .

    :

    x

    :carattributes

    y

    :

    pricey=g(x| )

    g

    (

    )

    model, parameters

    y

    =wx+w0

    y

    =w2x2

    +w1x+w0

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    Response surface design

    From Live Image quality database

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    Supervised learning

    Unsupervised learning

    Reinforcement learning

    Semi-supervised learning

    Active learning

    i d L i

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    supervised Learning

    U i d L i

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    )regularity(

    .

    ):)clustering

    )(

    )Learningmotifs(

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    Unsupervised Learning

    Density estimation

    )BSS(

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    )BSS(

    Microphone #1

    Microphone #2

    Speaker #1

    Speaker #2

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    Adopted from Dr. Andrew NG

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    Image Segmentation

    )(

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    )

    (...

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    Organizecomputingclusters

    Socialnetworkanalysis

    Imagecredit:NASA/JPLCaltech/E.Churchwell (Univ.ofWisconsin,Madison)

    AstronomicaldataanalysisMarket

    segmentation

    Adopted from Dr. Andrew NG

    semi-supervised Learning

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    semi-supervised Learning

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    Reinforcement Learning

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    Reinforcement Learning

    Game playing

    Robot in a maze

    Multiple agents, partial observability, ...

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    )Deeplearning(

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