Bar-Ilan University (אוניברסיטת בר-אילן) ** School Info Bar-Ilan University (אוניברסיטת בר-אילן) has 26 departments in Course Hero with 704 documents and 1 answered question.

Ramat Gan, Tel Aviv
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Bar-Ilan University Documents (704)

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46 pages
- 7Neural-Networks.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- 1 2 - : 100,000,000,000 10,000 3 4 ? ) ( )/ (
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7Neural-Networks.pdf
15 pages
- 2013fall_chap5_LinClassification.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :5 61. 61. 61. 61. SVM - 61. 616 61. 61. - * * * 61. * The Kernel Trick : ,HTF : 1. 1 DHS: 5.11, HTF: 4.5.2, 12.2-12.3 :
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2013fall_chap5_LinClassification.pdf
118 pages
- 5Anytime-learning-Decision-trees.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Anytime Algorithms for Learning Anycost Classifiers Saher Esmeir and Shaul Markovitch Computer Science Dept. Technion Thursday, April 12, 2012 1 Inductive Learning Tagged examples Learner Feature extractor <f1, f2, . , fn> Classifier Untagged object Saher
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5Anytime-learning-Decision-trees.pdf
10 pages
- 2012fall_chap9_boosting-2.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : :14 Bagging :Boosting - 11.1 11.1 Bagging 11.3 Adaboost - . data . .
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2012fall_chap9_boosting-2.pdf
50 pages
- 8linear_sep.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Linear Separators Assaf Glazer 1 March 2012 Motivation Examples: X xi , yi l , y 1, 1 i 1 2 + Linear separator or Decision boundary 1 2 March 2012 Linear Separators = 0.5,1 , = 1, then, a decision boundary is a solution for: 2 + w x b 0 Then, the
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8linear_sep.pdf
63 pages
- 3Decision-Trees.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
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3Decision-Trees.pdf
34 pages
- 1intro.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : : 1 12.3 19.3 26.3 2.4 16.4 30.4 7.5 14.5 21.5 28.5 4.6 11.6 18.6 25.6 / )(boosting / Case Studies -
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1intro.pdf
30 pages
- tutorial6-7.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Feature Selection Assaf Glazer 1 March 2012 Methods 2 Principle Component Analysis (PCA) Fishers Linear Discriminant Analysis (LDA) Feature Worth (FW) Information Gain (IG) March 2012 Feature Selection Motivation Improve performance of a classifier Rem
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tutorial6-7.pdf
10 pages
- 2011fall_chap4_classification.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :4 41. 412 413 : () 414 - : k-NN 415 1DHS(2001):2.1-2.7 : 4.1 : n n
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2011fall_chap4_classification.pdf
15 pages
- 2011fall_chap3_estimation.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :3 3.1 3.2 3.3 3.4 3.5 : - DHS:2001 : ( 3.1-3.4 ) ( 4.1-4.3 -), HTF:2001 ( 1.5.1 ). 3.1 : ) ,(modeling ) (inference
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2011fall_chap3_estimation.pdf
23 pages
- tutorial8.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Viola-Jones Face Detector Assaf Glazer 1 March 2012 Face Detection Motivation 2 March 2012 Prerequisite Knowledge 288 pixels Gray-level images are considered 384 pixels 3 March 2012 Background Presented by Paul Viola and Michael Jones in 2001 Allegedly,
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tutorial8.pdf
25 pages
- 9Bayesian-Learning.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : : )p(h h )p(D D )p(D|h )p(h|D D h - h D
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9Bayesian-Learning.pdf
65 pages
- 6Case-based learning.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : 1 Thursday, April 12, 2012 : , Shaul Markovitch 2012 2 Thursday, April 12, 2012 - - ,
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6Case-based learning.pdf
31 pages
- 2concept-learning.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : : 1 )(Concept O O O- 1 Shaul Markovitch 2009 2 : . : . : . :
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2concept-learning.pdf
12 pages
- 2013fall_chap7_regression_notes.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :7 51. 51. 513 51. 515 516 HTF: 2.7-2.9, 7.2, 7.10 : 5.1 ( ) x X ( 1) y Y
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2013fall_chap7_regression_notes.pdf
15 pages
- 2011fall_chap10_theory.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- :, , " :12 1211 1212 )(Empirical Risk Minimization 121. 121. VC 121. 1 , ,
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2011fall_chap10_theory.pdf
10 pages
- 2012fall_chap12_pca.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- (") , , :12 :12 1.21 . . : .
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2012fall_chap12_pca.pdf
36 pages
- tutorial3-4.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Statistical Tests Assaf Glazer 32 March 2012 Statistical Tests Scientific Knowledge 33 Explanation & Intuition Empirical Observation Formulate Hypothesis Test Hypothesis March 2012 Statistical Tests Basic Idea : ~ 95% Data Can we reject 34 95% (random
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tutorial3-4.pdf
9 pages
- 2013fall_chap2_bayes_notes.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :2 .DHS(2001):2.1-2.7 : . .2.2 ( ,)decision ( )measurement ( )states . , . . :
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2013fall_chap2_bayes_notes.pdf
35 pages
- 10Boosting.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : : : : , .
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10Boosting.pdf
8 pages
- tutorial5.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Linear Separators Assaf Glazer 1 March 2012 Motivation Examples: X = cfw_ xi , yi l , y cfw_1, +1 i =1 + Linear separator or Decision boundary - 2 March 2012 Linear Separators = 0.5,1 , = 1, then, a decision boundary is a solution for: + ( w x) + b =
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tutorial5.pdf
15 pages
- 2013fall_chap14_kmeans.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- 1/23/2014 Introduction to Machine Learning Fall 2013 K-means (14) Koby Crammer Department of EE Technion Material Section 10.4.3 Section 14.3.6 1 1/23/2014 Group Animals K-Means 4 2 1/23/2014 Its all about Assumptions! 5 Divide the objects into groups R G
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2013fall_chap14_kmeans.pdf
88 pages
- 4Decision-Trees-b.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- : 1 Thursday, April 12, 2012 - : ) ( , Shaul Markovitch 2012 2 Thursday, April 12, 2012
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4Decision-Trees-b.pdf
9 pages
- tutorial2.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- WEKA Tool Assaf Glazer 23 March 2012 WEKA ML Tool http:/www.cs.waikato.ac.nz/ml/weka/ Open source, written in JAVA Implements a wide range of ML methods 3 modes: GUI Command line JAVA API 24 March 2012 Before we start supervised vs. unsupervised le
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tutorial2.pdf
59 pages
- 2013fall_chap1_Introduction-2.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- ()046195 " , ? , , ? , , "? , , " ? , . , ? . , !
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2013fall_chap1_Introduction-2.pdf
22 pages
- 2013fall_chap6_NNs.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- " : , , :6 61. 61.1. 61.1. 61. 61.1. 61.1. * 61.1. 61. - 61.1. 61.1. - * 61.1. 61.14 614 * 1
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2013fall_chap6_NNs.pdf
21 pages
- tutorial1.pdf
- Bar-Ilan University
- Introduction to Machine Learning
- CS 89511 - Winter 2016
- Empirical Evaluation in Machine Learning Assaf Glazer 1 March 2012 Introduction to ML 3.0 credit points ML in practice 3-4 assignments No exam Programming skills - Required Assaf Glazer, Taub 733, [email protected] 3 March 2012 Empirical Evaluation Empiric
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tutorial1.pdf
8 pages
- תרגיל מספר 1- זיהוי מאפייני הארגון.docx
- Bar-Ilan University
- מחסני נתונים
- MBA 1101 - Fall 2017
- - 1 MyCheck - : :" : 021700554 036849677 ,04.12.16 : ," MyCheck - 60- ) ," , ( , ,
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תרגיל מספר 1- זיהוי מאפייני הארגון.docx
19 pages
- אורי הרצאה 5-9 + כנס יזמות.docx
- Bar-Ilan University
- מחסני נתונים
- MBA 1101 - Fall 2017
- " ' " 04 2016 - 5 ) ( )( . - value - proposition costumer- . ) .value proposition canvas ( . . , . - ? ?
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אורי הרצאה 5-9 + כנס יזמות.docx
45 pages
- תשובות מתוקנות.docx
- Bar-Ilan University
- רמב"ם
- MBA 5432 - Fall 2017
- " )(0309936 " ) , ,(5/1/17 ) (. . 25 , . .25 ,22 ,21 ,19 ,16 ,15 ,12 ,10 ,9 ,8 ,7 ,6 ,3 ,2 :
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תשובות מתוקנות.docx