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Home> Schools> University of Texas at Dallas> Computer Science (CS) > CS 6375> VC rectangle_circles

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UT Dallas CS 6375 - VC rectangle_circles

School name University of Texas at Dallas

Course Cs 6375- Machine Learning

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UT Dallas CS 6375 - VC rectangle_circles

School: University of Texas at Dallas

Course: Cs 6375- Machine Learning

Pages: 5

Documents in this Course

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05 - CBishop Chapter 1

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04 - Decision Trees

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03 - Inductive Learning

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01 - Introduction

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01 - cs229-notes1

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01 - A Checkers Learning Problem

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ensemble

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dtree

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clustering

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bayes

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svm-2

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svm-1

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regression

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naive_bayes

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mle

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mdp

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knn

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intro

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hmm-train

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hmm

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final_review

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featureselection

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evaluation

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hmm-train

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hmm

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final_review

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featureselection

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evaluation

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ensemble

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dtree

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9 pages

clustering

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bayes

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svm-2

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svm-1

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regression

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NeuralNets

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naive_bayes

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mle

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mdp

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knn

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intro

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svm-2

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svm-1

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regression

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NeuralNets

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naive_bayes

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mle

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mdp

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knn

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intro

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hmm-train

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hmm

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final_review

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featureselection

featureselection

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evaluation

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ensemble

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17 pages

dtree

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9 pages

clustering

23 pages

bayes

19 pages

24 pages

svm-2

16 pages

svm-1

18 pages

18 pages

regression

16 pages

NeuralNets

26 pages

naive_bayes

20 pages

mle

16 pages

mdp

19 pages

knn

11 pages

intro

19 pages

hmm-train

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hmm

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final_review

10 pages

featureselection

featureselection

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evaluation

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ensemble

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17 pages

dtree

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9 pages

clustering

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bayes

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backprop_sol

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hw2

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hw1

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hw0

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hw6-PartII

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hw5

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hw4-PartII

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hw4-PartI

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hw3

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20.mdp

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19.em

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18.clustering

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17.regression

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16.svm-2

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15.svm-1

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14.vc

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13.ensemble

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12.featureselection

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11.evaluation

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10.hmm-train

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9.hmm

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8.CrossValidation

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7.neuralNets

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6.nearest neighbour

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5.mle

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4.naive_bayes

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Back-Propagation-Example2

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exam_sample1

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MLHW3

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MLHW4

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IISC_VC dimension

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AdaBoostExample

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VC-Example2-solutions

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backprop_sol

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midterm_practice

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hw4-PartII

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hw4-PartI

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hw3

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hw2

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hw1

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10.hmm-train

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9.hmm

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8.CrossValidation

8.CrossValidation

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7.neuralNets

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6.nearest neighbour

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5.mle

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4.naive_bayes

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3.bayes

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2.dtree

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1.intro

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15.svm-1

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14.vc

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13.ensemble

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12.featureselection

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11.evaluation

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hw2

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hw1

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hw0

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hw4-PartII

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9.hmm

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7.neuralNets

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6.nearest neighbour

6.nearest neighbour

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5.mle

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3.bayes

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2.dtree

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1.intro

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12.featureselection

12.featureselection

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11.evaluation

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10.hmm-train

26 pages

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