• 16 Pages class16
    Class16

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 16 Learning Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administration Midterm: Wednesday, March 17, 2004 In class Closed book Material covered by Spr

  • 536 Pages boosting
    Boosting

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvips 5.516a Copyright 1986, 1993 Radical Eye Software %Title: submit.dvi %CreationDate: Fri Apr 5 14:37:31 1996 %Pages: 9 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Italic Times-Bold Times-Roman Helve

  • 18 Pages class18
    Class18

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 18 Density estimation with hidden variables and missing values Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Project proposals Due: Wednesday, March 24, 2004 1-2 pages long Propos

  • 14 Pages class17
    Class17

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 17 Density estimation with hidden variables and missing values Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administration Midterm: Wednesday, March 17, 2004 In class Closed boo

  • 16 Pages class6
    Class6

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 6 Density estimation III. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Density estimation: Binomial distribution Multinomial distribution Normal distribution

  • 12 Pages class11
    Class11

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 11 Support vector machines Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Linearly separable classes. Algorithms. Support vector machines Maximum margin hyperpla

  • 14 Pages class23
    Class23

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 23 Dimensionality reduction Feature selection Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Dimensionality reduction. Motivation. Classification problem example: We have an input

  • 485 Pages RLsurvey
    RLsurvey

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: rl-survey.dvi %Pages: 41 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips -o rl-survey.ps rl-survey %DVIPSParameters: dpi=300, c

  • 1773 Pages boostingmargin
    Boostingmargin

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: ml97.dvi %Pages: 30 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Helvetica %EndComments %DVIPSCommandLine: dvi

  • 393 Pages arcing
    Arcing

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-3.0 %Title: (arc97) %Creator: (Microsoft Word: PSPrinter 8.3.1; massaged by hand: scf@stat.Berkeley.EDU) %CreationDate: (11:49 AM Monday, July 14, 1997) %For: () %Pages: 23 %DocumentFonts: Palatino-Roman Palatino-Bold Palatino-Italic Palat

  • 312 Pages Jordan-hier-mix-experts
    Jordan-hier-mix-experts

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvips 5.516 Copyright 1986, 1993 Radical Eye Software %Title: top.dvi %CreationDate: Sat May 13 22:55:31 1995 %Pages: 36 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: dvips -o top.ps top %DVIPSS

  • 13 Pages class20
    Class20

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 20a Ensamble methods. Mixtures of experts Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Mixture of experts model Ensamble methods: Use a combination of simpler learners to improv

  • 238 Pages Heckerman
    Heckerman

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvips 5.54 Copyright 1986, 1994 Radical Eye Software %Title: kddj.dvi %CreationDate: Fri Oct 24 07:32:48 1997 %Pages: 58 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSCommandLine: C:\TEX\DVIPS.EXE kddj %DVIP

  • 707 Pages burges-svm98
    Burges-svm98

    School: Pittsburgh

    Course: Machine Learning

    %!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: final.dvi %Pages: 43 %PageOrder: Ascend %BoundingBox: 0 0 596 842 %DocumentPaperSizes: a4 %EndComments %DVIPSCommandLine: dvips -f final %DVIPSParameters: dpi=600

  • 26 Pages class14
    Class14

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 14 Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D = {D1 , D2 ,., Dn } Di = x i a vector of attribute values Attributes: modeled

  • 15 Pages class23
    Class23

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 23 Ensemble methods. Bagging and Boosting Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administrative announcements Term projects: Reports due on Wednesday, April 21, 2004 at 12

  • 13 Pages class2
    Class2

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 2 Designing a learning system Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Design of a learning system (first view) Data Mode

  • 11 Pages class10
    Class10

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 10 Multi-layer neural networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Linear units Linear regression Logistic regression f (x) = p( y = 1 | x, w) = g (wT x) 1 w0 w0 f (x)

  • 15 Pages class6
    Class6

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 6 Linear regression Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administration Matlab: Statistical and neural network toolboxes are not available on unixs machines Please use

  • 20 Pages class3
    Class3

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 3 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcements Next lecture: Matlab tutorial Rules for attending the class: Registered for credit Registered fo

  • 17 Pages class5
    Class5

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 5 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcements Homework 2 Due on Wednesday before the class Reports: hand in before the class Programs: submit

  • 23 Pages class2
    Class2

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 2 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu

  • 16 Pages class2
    Class2

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 2 Designing a learning system Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Typical learning Three basic steps: Select a mode

  • 18 Pages class22
    Class22

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 22 Clustering Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Gaussian mixture model Probability of occurrence of a data example x is modeled as p ( x ) = p (C = i ) p (x | C = i

  • 20 Pages class12
    Class12

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 12 Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Density estimation Data: D = {D1 , D2 ,., Dn } Di = x i a vector of attribute values Attributes: modeled

  • 14 Pages class21
    Class21

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 21 Ensemble methods. Boosting Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administrative announcements Term projects: Reports due on Wednesday, April 23, 2003 at 2pm. Presenta

  • 13 Pages class18
    Class18

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 18 Dimensionality reduction Feature selection Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Dimensionality reduction. Motivation. Classification problem example: We have an input

  • 15 Pages class14
    Class14

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 14 Support vector machines Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Fisher Linear Discriminant Algorithms for linear decision boundary Support vector mach

  • 16 Pages class13
    Class13

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 13 Multi-way classification Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administrative announcements Homework 6 due on Wednesday Plan for the upcoming month: Homework 7 due on

  • 12 Pages class1
    Class1

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu

  • 8 Pages class1
    Class1

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu

  • 12 Pages class4
    Class4

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 4 Density estimation Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcements Homework 1 Due on Wednesday before the class Reports: hand in before the class Programs: submit

  • 16 Pages class13
    Class13

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 13 Bayesian belief networks. Inference. Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Midterm exam Monday, March 17, 2003 In class Closed book Material covered by Wednesday, Mar

  • 11 Pages class10
    Class10

    School: Pittsburgh

    Course: Machine Learning

    CS 1571 Introduction to AI Lecture 10 Multi-layer neural networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 1571 Intro to AI Linear units Linear regression Logistic regression f (x) = p( y = 1 | x, w) = g (wT x) 1 w0 f (x) = w T

  • 13 Pages class3
    Class3

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 3 Evaluation of predictors Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Homework 1. Due next week on Wednesda

  • 15 Pages class14
    Class14

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 14 Learning Bayesian belief networks Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administration Midterm: Monday, March 17, 2003 In class Closed book Material covered by Wednes

  • 19 Pages class22
    Class22

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 22 Reinforcement learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Administrative announcements Term projects: Reports due on Wednesday, April 23, 2003 at 2pm. Presentatio

  • 3 Pages homework-3
    Homework-3

    School: Pittsburgh

    Course: Machine Learning

    H v H H # H ( % ( ` % I 9 1UT 9 9 exsC}T&H&TP 9 ( 9 9 XE A 4V A &4X$xVtT&C#$ 9 XTC%W 9 e$VnT 9 }C sC%W 9 9 &Tes$X A &T&XV&w$nChW 9 4&% ( H # ' % ( H t ( H r t W # r ( ' ph&jjaVw|whs

  • 11 Pages class11
    Class11

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 11 Support vector machines Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Outline Outline: Support vector machines Linearly separable classes. Algorithms. Maximum margin hyperpla

  • 10 Pages class15
    Class15

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 15 Density estimation with hidden variables and missing values Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Project proposals Due: Monday, March 24, 2003 1-2 pages long Proposal

  • 18 Pages class1
    Class1

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square, x4-8845 http:/www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning Administration Study material Handouts, your notes and cours

  • 7 Pages class19
    Class19

    School: Pittsburgh

    Course: Machine Learning

    CS 2750 Machine Learning Lecture 19 Decision trees Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square CS 2750 Machine Learning Announcement Term project: Reports due on Wednesday, April 23 at 2pm Project presentations: When: Friday, April

  • 41 Pages Dietterich_AIMag18-04-010
    Dietterich_AIMag18-04-010

    School: Pittsburgh

    Course: Machine Learning

    This Is a Publication of The American Association for Artificial Intelligence This electronic document has been retrieved from the American Association for Artificial Intelligence 445 Burgess Drive Menlo Park, California 94025 (415) 328-3123 (415) 3

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