Search results for "Lecture" - Rhea

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Page title matches

• Lecture 19 - Nearest Neighbor Error Rates OldKiwi
  =Lecture 19 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  6 KB (1,041 words) - 11:22, 10 June 2013
• Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
  =Lecture 20 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  7 KB (1,082 words) - 11:23, 10 June 2013
• Lecture 21 - Decision Trees(Continued) OldKiwi
  =Lecture 21 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  7 KB (1,055 words) - 11:23, 10 June 2013
• Lecture 22 - Decision Trees and Clustering OldKiwi
  =Lecture 22 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  6 KB (837 words) - 11:23, 10 June 2013
• Lecture 23 - Spanning Trees OldKiwi
  =Lecture 23 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  7 KB (1,091 words) - 11:23, 10 June 2013
• Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
  =Lecture 24 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  9 KB (1,276 words) - 11:24, 10 June 2013
• Lecture 25 - Clustering Algorithms OldKiwi
  =Lecture 25 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  8 KB (1,299 words) - 11:24, 10 June 2013
• Lecture 11 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 11, [[ECE264]], Spring 2012, Prof. Lu=
  3 KB (327 words) - 05:23, 11 July 2012
• Lecture 12 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 12, [[ECE264]], Spring 2012, Prof. Lu=
  1 KB (232 words) - 05:24, 11 July 2012
• Lecture 13 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 13, [[ECE264]], Spring 2012, Prof. Lu=
  7 KB (1,158 words) - 05:24, 11 July 2012
• Lecture 13-2/21/2012-Kailu Song lecturen notes
  Lecture 13_ notes_ Kailu Song
  1 KB (155 words) - 12:30, 27 February 2012
• Lecture 14 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 14, [[ECE264]], Spring 2012, Prof. Lu=
  3 KB (506 words) - 05:25, 11 July 2012
• Lecture 15 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 15, [[ECE264]], Spring 2012, Prof. Lu=
  6 KB (987 words) - 05:25, 11 July 2012
• LECTURE NOTES 2/28 Kailu Song
  Lecture notes_2/28_Kailu Song
  839 B (114 words) - 11:37, 28 February 2012
• Lecture 26 - Statistical Clustering Methods OldKiwi
  =Lecture 26 Lecture notes= [[Lecture 1 - Introduction_OldKiwi|1]]|
  8 KB (1,214 words) - 11:24, 10 June 2013
• Lecture 27 - Clustering by finding valleys of densities OldKiwi
  [[Category:lecture notes]] =Lecture 27 Lecture notes=
  8 KB (1,313 words) - 11:24, 10 June 2013
• Lecture 28 - Final lecture OldKiwi
  [[Category:lecture notes]] =Lecture 28 Lecture notes=
  10 KB (1,704 words) - 11:25, 10 June 2013
• Lecture 17 Mar9
  //Lecture Fri 9Mar //Lecture 17
  2 KB (312 words) - 08:58, 9 March 2012
• Lecture 18 ECE264F12Lu
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 18, [[ECE264]], Spring 2012, Prof. Lu=
  7 KB (993 words) - 05:25, 11 July 2012
• Lecture Prof. Elmqvist 3/21 ipa 2-1
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 21, [[ECE264]], Spring 2012, Prof. Lu=
  1 KB (167 words) - 06:01, 11 July 2012
• Lecture 19
  =Lecture 19=
  3 KB (488 words) - 09:58, 22 March 2012
• Lecture 20
  [[Category:ECE264]] [[Category:Programming]] [[Category:C]] [[Category:lecture notes]] =Lecture 20, [[ECE264]], Spring 2012, Prof. Lu=
  6 KB (794 words) - 05:26, 11 July 2012
• Lecture 27
  Hanye Xu April 20 Lecture 26
  2 KB (210 words) - 19:10, 19 April 2012
• Lecture for IPA2-5
  2 KB (340 words) - 05:08, 25 April 2012
• ECE637 Bouman lecture notes Foreword mhossain
  [[Category:lecture notes]] These notes were taken while watching Professor Charles Bouman's lecture series on Digital Image Processing ([[ECE637|ECE 637]]). Whereas the origin
  2 KB (311 words) - 07:22, 26 February 2014
• ECE301 signals systems lecture notes
  [[Category:lecture notes]] Lecture Notes
  3 KB (328 words) - 17:57, 30 November 2018
• ECE302 probability lecture notes
  [[Category:lecture notes]] Lecture Notes
  2 KB (292 words) - 08:02, 15 May 2013
• MA375 discrete math lecture notes
  [[Category:lecture notes]] Lecture Notes
  1 KB (167 words) - 09:33, 20 May 2013
• Lecture Schedule ECE438Fall13 Boutin
  .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.1_signal_types.pdf Signal Types]
  9 KB (1,353 words) - 09:04, 11 November 2013
• ECE438 digital signal processing lecture notes
  [[Category:lecture notes]] Lecture Notes
  2 KB (189 words) - 08:26, 11 November 2013
• Lecture 3 - Bayes Theorem and its Application to Pattern Recognition OldKiwi
  70 B (10 words) - 07:10, 12 February 2014
• Lecture Schedule ECE438Fall14 Boutin
  .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes
  9 KB (1,320 words) - 04:46, 11 September 2014
• Lecture Schedule ECE438Fall15 Boutin
  .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes
  10 KB (1,356 words) - 13:19, 19 October 2015
• Lecture Schedule ECE438Fall16 Boutin
  .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes
  10 KB (1,357 words) - 17:02, 14 September 2016
• Lecture Schedule ECE438Spring17 Boutin
  .... Allebach's lecture notes on [http://dynamo.ecn.purdue.edu/~bouman/ece438/lecture/module_1/1.1_signals/1.1.5_complex_variables.pdf complex variables] **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes
  10 KB (1,357 words) - 09:45, 8 January 2017
• Lecture Schedule ECE301Spring18 Boutin
  ! colspan="5" style="background: #e4bc7e; font-size: 150%;" | Tentative Lecture Schedule, [[ECE301]] Spring 2018, [[user:mboutin|Prof. Boutin]] | '''Lecture'''
  3 KB (432 words) - 09:38, 8 January 2018
• Lecture Schedule ECE438Fall2019 Boutin
  **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes **Prof. Allebach's lecture notes on [https://engineering.purdue.edu/~ece438/lecture_notes/legacy_notes
  10 KB (1,356 words) - 18:52, 20 August 2019

Page text matches

• Lecture 3 - Bayes classification Old Kiwi
  =Lecture 3, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  5 KB (843 words) - 08:46, 17 January 2013
• Lecture 4 - Bayes Classfication Old Kiwi
  #REDIRECT: [[Lecture 4 - Bayes Classification_Old Kiwi]]
  56 B (7 words) - 11:37, 17 March 2008
• Lecture 5 - Discriminant Functions Old Kiwi
  =Lecture 5, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  6 KB (916 words) - 08:47, 17 January 2013
• Lecture 6 - Discriminant Functions Old Kiwi
  =Lecture 6, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  9 KB (1,586 words) - 08:47, 17 January 2013
• Lecture 7 - MLE and BPE Old Kiwi
  =Lecture 7, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  10 KB (1,488 words) - 10:16, 20 May 2013
• Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
  =Lecture 8, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  5 KB (792 words) - 08:48, 17 January 2013
• Lecture 9 - Linear Discriminant Functions Old Kiwi
  =Lecture 9, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  8 KB (1,307 words) - 08:48, 17 January 2013
• Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi
  =Lecture 10, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  5 KB (755 words) - 08:48, 17 January 2013
• Lecture 11 - Fischer's Linear Discriminant again Old Kiwi
  =Lecture 11, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  5 KB (907 words) - 08:49, 17 January 2013
• Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi
  =Lecture 12, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  8 KB (1,235 words) - 08:49, 17 January 2013
• Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
  =Lecture 13, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  8 KB (1,354 words) - 08:51, 17 January 2013
• Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
  =Lecture 14, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  13 KB (2,073 words) - 08:39, 17 January 2013
• Lecture 15 - Parzen Window Method Old Kiwi
  =Lecture 15, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  7 KB (1,212 words) - 08:38, 17 January 2013
• Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
  [[Category:Lecture Notes]] =Lecture 16, [[ECE662]]: Decision Theory=
  10 KB (1,607 words) - 08:38, 17 January 2013
• Bayesian Parameter Estimation Old Kiwi
  Complement to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE Back to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE
  4 KB (707 words) - 10:37, 20 May 2013
• Lecture 2 Old Kiwi
  #REDIRECT: [[Lecture 2 - Decision Hypersurfaces_Old Kiwi]]
  58 B (7 words) - 18:07, 6 March 2008
• Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi
  =Lecture 18, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  6 KB (1,066 words) - 08:40, 17 January 2013
• Support Vector Machines Old Kiwi
  ...and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|Lecture 13]].
  3 KB (366 words) - 08:48, 10 April 2008
• ECE662:ChangeLog Old Kiwi
  ...d [[Lecture 3 - Bayes classification_Old Kiwi]] by adding the section on [[Lecture 3 - Bayes classification_Old Kiwi#Bayes_rule|Bayes rule]] equation <3,4,5> * 2008/04/17 -- Added last part of Lecture on 26 [[Statistical Clustering Methods_Old Kiwi]]
  10 KB (1,418 words) - 12:21, 28 April 2008
• What is Pattern Recognition Old Kiwi
  (From [[Lecture 1 - Introduction_Old Kiwi]])
  3 KB (436 words) - 08:43, 10 April 2008
• Bayesian Decision Theory Old Kiwi
  Lectures discussing Bayesian Decision Theory : [[Lecture 3_Old Kiwi]] and [[Lecture 4_Old Kiwi]]
  3 KB (558 words) - 17:03, 16 April 2008
• Artificial Neural Networks Old Kiwi
  ...re_14_-_ANNs%2C_Non-parametric_Density_Estimation_(Parzen_Window)_Old_Kiwi|Lecture 14]] ...p://www.ir.iit.edu/~nazli/cs422/CS422-Slides/DM-NeuralNetwork.pdf In-depth Lecture Slides]
  2 KB (308 words) - 18:24, 22 October 2010
• Parametric Estimators Old Kiwi
  ...]] and [[Lecture_8_-_MLE%2C_BPE_and_Linear_Discriminant_Functions_Old_Kiwi|Lecture 8]] Back to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE
  6 KB (995 words) - 10:39, 20 May 2013
• ECE637:BoumanSpring08 Old Kiwi
  Class Lecture Notes for EE637 * Class Lecture Notes for EE637
  646 B (97 words) - 21:22, 1 May 2008
• Publications Old Kiwi
  ...uthentication for access control by visual tracking and face recognition", Lecture Notes in Computer Science, Springer, Vol. 1206, pp. 177-183, 1997.'''
  39 KB (5,715 words) - 10:52, 25 April 2008
• Comparison of MLE and Bayesian Parameter Estimation Old Kiwi
  Complement to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE Back to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE
  2 KB (287 words) - 10:39, 20 May 2013
• Derivation of Fisher's Linear Discriminant Old Kiwi
  ([[Lecture 11_Old Kiwi]] begins here)
  4 KB (653 words) - 10:50, 22 April 2008
• Lecture 3 Old Kiwi
  #REDIRECT: [[Lecture 3 - Bayes classification_Old Kiwi]]
  56 B (7 words) - 11:33, 17 March 2008
• Lecture 4 Old Kiwi
  #REDIRECT:[[Lecture 4 - Bayes Classification_Old Kiwi]]
  55 B (7 words) - 11:34, 17 March 2008
• Lecture 4 - Bayes Classification Old Kiwi
  =Lecture 4, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  8 KB (1,360 words) - 08:46, 17 January 2013
• Decision Surfaces Old Kiwi
  (From [[Lecture 1 - Introduction_Old Kiwi]])
  1 KB (220 words) - 11:45, 17 March 2008
• Lecture 7 Old Kiwi
  #redirect: [[Lecture 7 - MLE and BPE_Old Kiwi]]
  47 B (8 words) - 12:09, 17 March 2008
• Lecture 8 Old Kiwi
  #redirect:[[Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi]]
  77 B (11 words) - 12:10, 17 March 2008
• Bayes Decision Rule Old Kiwi
  [[Category:lecture notes]] for [[Lecture_6_-_Discriminant_Functions_OldKiwi|Lecture 6, ECE662, Spring 2010]]
  1 KB (172 words) - 11:08, 10 June 2013
• Maximum Likelihood Estimation Old Kiwi
  Complement to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE Back to [[Lecture_7_-_MLE_and_BPE_OldKiwi|Lecture 7: Maximum Likelihood Estimation and Bayesian Parameter Estimation]], [[ECE
  3 KB (465 words) - 10:37, 20 May 2013
• Fisher Linear Discriminant Old Kiwi
  ...of the two classes while minimizing the variance within each class. See [[Lecture 10_Old Kiwi]] for detailed explanation.
  3 KB (475 words) - 18:05, 28 March 2008
• Lecture 11 Old Kiwi
  #redirect: [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi]]
  72 B (9 words) - 16:20, 25 March 2008
• Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
  =Lecture 19, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  5 KB (1,003 words) - 08:40, 17 January 2013
• Perceptron Old Kiwi
  ...in [[Lecture_10_-_Batch_Perceptron_and_Fisher_Linear_Discriminant_OldKiwi|Lecture 10]].
  3 KB (528 words) - 13:52, 2 February 2012
• Perceptron Convergence Theorem Old Kiwi
  ...ttern_Recognition_Decision_Making_Processes_Spring2008_sLecture_collective|lecture notes]]) ...cture_10_-_Batch_Perceptron_and_Fisher_Linear_Discriminant_OldKiwi|Back to Lecture 10]]
  3 KB (446 words) - 06:36, 21 April 2013
• Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
  =Lecture 20, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  6 KB (1,047 words) - 08:42, 17 January 2013
• References Old Kiwi
  * [[Lecture Notes_Old Kiwi]]
  607 B (80 words) - 14:27, 30 March 2008
• Curse of Dimensionality Old Kiwi
  [[Category:lecture notes]] from [[Lecture_2_-_Decision_Hypersurfaces_OldKiwi|Lecture 2, ECE662, Spring 2010]]
  831 B (120 words) - 10:57, 10 June 2013
• MLE Examples: Exponential and Geometric Distributions Old Kiwi
  Complement to [[Lecture_7_Old_Kiwi|Lecture 7: "Comparison of Maximum likelihood (MLE) and Bayesian Parameter Estimatio Back to [[Lecture_7_Old_Kiwi|Lecture 7: "Comparison of Maximum likelihood (MLE) and Bayesian Parameter Estimatio
  3 KB (498 words) - 10:13, 20 May 2013
• Lecture 21 - Decision Trees(Continued) Old Kiwi
  =Lecture 21, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  6 KB (1,012 words) - 08:42, 17 January 2013
• Lecture 22 - Decision Trees and Clustering Old Kiwi
  =Lecture 22, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  6 KB (806 words) - 08:42, 17 January 2013
• Maximum A Posteriori Old Kiwi
  ...with Bayesian probability theory, but it works reasonably well. (See Also: Lecture 7 and [BPE]?)
  393 B (57 words) - 01:29, 7 April 2008
• Impurity Old Kiwi
  ...that class. It is zero when all training data belongs to one class. See [[Lecture 21 - Decision Trees(Continued)_Old Kiwi]] for the definitions of different
  248 B (42 words) - 10:17, 7 April 2008
• Lecture 23 - Spanning Trees Old Kiwi
  =Lecture 23, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  7 KB (1,060 words) - 08:43, 17 January 2013
• Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
  =Lecture 24, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
  8 KB (1,254 words) - 08:43, 17 January 2013