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  • ECE 301 (BoutinFall2007)
    === Lecture Notes and Class Feedback=== * [[ECE 301 Fall 2007 mboutin Lecture 100|Lecture 100 (12/04/08)]]
    2 KB (238 words) - 16:54, 23 April 2013
  • ECE661Fall2008Kak
    ==Other Course Material (from handouts and lecture)==
    14 KB (2,253 words) - 12:21, 9 January 2009
  • Ideas for Instructors
    ...example catching some mistakes or misconceptions. An alternative to typing lecture notes is to set up a page where students can offer to trade notes with each
    6 KB (1,060 words) - 13:12, 6 May 2012
  • Rhea Course Template 1
    === Lecture Notes and Class Feedback=== * [[XYZ 123 professor jsmith Semester Year Lecture 1|Lecture 1]]
    2 KB (337 words) - 07:30, 5 January 2009
  • Rhea Portal
    *[[:Category:lecture_notes|Lecture Notes]]: taken by students and neatly typed.
    6 KB (877 words) - 07:22, 21 March 2013
  • ECE 301 (SanSummer2008)
    Lecture: M T W Th F 9:50 am - 10:50 am @ EE 117 #Lecture 1
    7 KB (921 words) - 06:08, 21 October 2011
  • ECE301:SanSummer08:Exam II (ECE301Summer2008asan)
    Lecture 15 pg. 18 gives details of the properties for these types of functions.
    4 KB (815 words) - 10:57, 21 November 2008
  • ECE 301 Fall 2007 mboutin Course Notes
    [[Category:lecture Notes]]
    2 KB (229 words) - 16:55, 23 April 2013
  • ECE 301 Fall 2007 mboutin Lecture 100
    *[[Image:ECE_301_Fall_2007_Lecture12_04.pdf|Lecture 100 - 12/04/08]]
    68 B (7 words) - 11:21, 5 December 2008
  • ECE438 (BoutinSpring2009)
    * [http://cobweb.ecn.purdue.edu/~allebach/ece438/lecture/ Prof. Allebach's Lecture Notes]
    9 KB (1,235 words) - 10:15, 11 January 2010
  • Rhea Course Template 3
    === Lecture Notes and Class Feedback=== * [[XYZ 123 professor jsmith Semester Year Lecture 1|Lecture 1]]
    1 KB (202 words) - 07:35, 12 August 2009
  • ECE 438 professor Boutin Spring 2009 Course Details
    * [[Lecture Schedule (ECE438BoutinSpring09)|Schedule]]
    558 B (80 words) - 09:55, 27 February 2009
  • ECE201 (Spring2009)
    === Lecture Notes and Class Feedback===
    1 KB (178 words) - 09:11, 26 February 2009
  • Main Page MA181Fall2008bell
    ==[[Lecture Notes_MA181Fall2008bell]]==
    3 KB (390 words) - 06:35, 10 August 2010
  • MA453 (WaltherSpring2009)
    You may bring: lecture notes, homework assignments.
    4 KB (704 words) - 10:52, 16 December 2009
  • Main Page ECE301Fall2008mboutin
    Lecture: '''MWF 4:30PM''' in EE129 === Lecture Notes and Class Feedback===
    5 KB (720 words) - 06:10, 16 September 2013
  • Main Page MA375Fall2008walther
    ==Lecture Notes==
    10 KB (1,377 words) - 14:49, 20 September 2012
  • ECE637 (BoumanSpring2009)
    = Lecture Notes = * [[Lecture1NotesECE637BoumanSpring09|Lecture 1: Introductions]]
    1 KB (200 words) - 08:03, 9 April 2013
  • Lecture2ECE400S14
    =Lecture 2 Blog, [[ECE400]] Spring 2014, [[user:mboutin|Prof. Boutin]]= Quick link to lecture blogs: [[Lecture1_blog_ECE400S14|1]] [[Lecture2ECE400S14|2]] [[Lecture3ECE
    997 B (127 words) - 13:32, 23 January 2014
  • ECE 438 Spring 2009 mboutin Course Notes
    [[Category:lecture notes]] =Lecture Notes for [[ECE438]] Spring 2009, [[user:mboutin|Prof. Boutin]]=
    865 B (78 words) - 06:37, 16 September 2013
  • Lecture Questions
    == [[ECE264]] Lecture Questions, Spring 2009, Prof. Lu ==
    2 KB (331 words) - 05:39, 11 July 2012
  • ECE302 (ChihwSpring2009)
    * [http://cobweb.ecn.purdue.edu/~chihw/ECE302_09S.html Lecture Notes and HW pdfs]
    2 KB (279 words) - 21:17, 31 March 2009
  • MA 375 Spring 2009 Walther Definitions
    [[Category:lecture notes]]
    5 KB (850 words) - 08:41, 20 May 2013
  • CourseNotes1 (BoutinSpring2009)
    [[Category:lecture notes]]
    3 KB (532 words) - 06:43, 16 September 2013
  • CourseNotes2 (BoutinSpring2009)
    [[Category:lecture notes]]
    3 KB (508 words) - 06:43, 16 September 2013
  • CourseNotes3 (BoutinSpring2009)
    [[Category:lecture notes]]
    2 KB (408 words) - 06:43, 16 September 2013
  • CourseNotes4 (BoutinSpring2009)
    [[Category:lecture notes]] ...">Use of diagrams makes this difficult to add in, please see previous days lecture notes for the properties of Linearity and Time-Invariance.</font>
    2 KB (359 words) - 06:43, 16 September 2013
  • Chapter 3: Problem 10
    ...ty that aren't factors of 20? I think this is correct from what he said in lecture and since 2 is a multiple of 20 then that can't be used along with examples
    1 KB (184 words) - 13:10, 4 February 2009
  • MA 375 Spring 2009 Walther Week 2
    [[Category:lecture notes]]
    2 KB (407 words) - 08:43, 20 May 2013
  • CourseNotes6 (BoutinSpring2009)
    [[Category:lecture notes]]
    769 B (105 words) - 06:44, 16 September 2013
  • Program for U Groups
    [[Category:lecture notes]]
    2 KB (269 words) - 08:38, 20 May 2013
  • CourseNotes16 (BoutinSpring2009)
    [[Category:lecture notes]]
    2 KB (376 words) - 06:44, 16 September 2013
  • MA 375 Spring 2009 Walther Week 5
    [[Category:lecture notes]]
    576 B (70 words) - 09:18, 20 May 2013
  • HW 5 Question 1
    ...re/module_1/1.5_z_transform/1.5.4_zt_and_ccf_diff_eq.pdf Allebach's useful lecture note] for this problem.--[[User:Kim415|Kim415]] 16:07, 1 March 2009 (UTC)
    2 KB (295 words) - 13:44, 2 March 2009
  • CourseNotes20 (BoutinSpring2009)
    [[Category:lecture notes]]
    3 KB (522 words) - 06:45, 16 September 2013
  • CourseNotes30 (BoutinSpring2009)
    [[Category:lecture notes]] ...sources of digital interference in camera systems that we didn't get to in lecture but i'll go ahead and point out a few of them that you have actually seen i
    2 KB (324 words) - 06:45, 16 September 2013
  • Course evaluation (ECE438BoutinSpring2009)
    ...ful, but there should be a balance between theory and concrete examples in lecture. --[[User:Leedj|Leedj]] 17:03, 3 May 2009 (UTC)
    4 KB (709 words) - 13:42, 3 May 2009
  • Reading guide (ECE438BoutinSpring2009)
    .... 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]
    8 KB (1,226 words) - 11:40, 1 May 2009
  • ECE301 (HuffmalmSummer2009)
    * The Final will be held Wednesday, August 5, 8-9:30AM. The location is the lecture room. Exam information posted on the [[Exams/Quizzes]] page. '''Note that
    2 KB (389 words) - 07:32, 7 September 2010
  • ECE301
    [[ECE301_signals_systems_lecture_notes|Lecture Notes]]
    720 B (90 words) - 12:11, 27 March 2015
  • ECE438 (BoutinFall2009)
    * [[ECE_438_Fall_2009_mboutin_DFT_lecture_material|Some material for the lecture on DFT (re:leakage effect)]] * [http://cobweb.ecn.purdue.edu/~allebach/ece438/lecture/ Prof. Allebach's Lecture Notes]
    7 KB (1,067 words) - 12:05, 25 June 2010
  • Main Page ECE302Fall2008sanghavi
    * [[Lecture 5_ECE302Fall2008sanghavi|Lecture 5: Independence]] * [[10/27 Lecture: Merkov and Chebyshev Inequalities_ECE302Fall2008sanghavi]]
    5 KB (663 words) - 13:02, 22 November 2011
  • Lecture 6 ECE302Fall2008sanghavi
    #REDIRECT [[Lecture 5_ECE302Fall2008sanghavi]]
    46 B (5 words) - 12:49, 8 September 2008
  • 5.1 - Virgil Hsieh ECE302Fall2008sanghavi
    Hey, way to point out something that wasn't mentioned in lecture! It's not like that stuff was directly out of the notes or anything. Note m
    449 B (84 words) - 04:09, 6 October 2008
  • 7.4 Katie Pekkarinen ECE302Fall2008sanghavi
    ...two independent random variables is the convolution of the two PDFs. The lecture notes from 10/10 are helpful.
    133 B (23 words) - 19:13, 19 October 2008
  • Spencer Mitchell 8.4 ECE302Fall2008sanghavi
    Something small that I didn't pick up on in lecture.
    227 B (40 words) - 08:42, 4 November 2008
  • Problem 4 - Junzhe Geng ECE302Fall2008sanghavi
    This problem is quiet similar with the lecture example, except for the [-a,a] instead of [0,a]
    309 B (65 words) - 12:32, 10 November 2008
  • Problem 2 - Steven Streeter ECE302Fall2008sanghavi
    I was looking back at the 11-05-08 lecture and noticed the coin w/ unknown bias example where Pr[H] = P in n tosses.
    601 B (122 words) - 18:42, 10 November 2008
  • Map Estimation ECE302Fall2008sanghavi
    [[Category:lecture notes]]
    4 KB (671 words) - 09:23, 10 May 2013
  • Lecture Notes MA181Fall2008bell
    [http://www.math.purdue.edu/~bell/MA181/Lectures Bell's lecture notes] It would be great if people from class posted their lecture notes here from time to time too. --[[User:Bell|Bell]] 12:07, 2 October 20
    753 B (128 words) - 11:13, 2 October 2008
  • Useful Formulas MA181Fall2008bell
    ...ify the integral. It's a case that I don't think we covered in reading or lecture, but it did come up in a homework problem. Using the double-angle identity
    3 KB (584 words) - 10:12, 21 October 2008
  • A Baffling Fact MA181Fall2008bell
    After today's lecture, I can readily see why <math>i^i</math> has infinitely many values, as ther
    4 KB (634 words) - 05:44, 23 September 2011
  • 2008/09/04 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    4 KB (721 words) - 08:18, 20 May 2013
  • 2008/09/02 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    963 B (153 words) - 08:19, 20 May 2013
  • 2008/08/28 MA375Fall2008walther
    Lecture Notes
    686 B (105 words) - 08:08, 20 May 2013
  • 2008/09/09 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    3 KB (460 words) - 08:18, 20 May 2013
  • Ideas for posting MA375Fall2008walther
    * Outline the lecture, and/or summarize a point from it.
    2 KB (310 words) - 18:29, 17 September 2008
  • 2008/09/16 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    685 B (108 words) - 08:17, 20 May 2013
  • 2008/09/18 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    860 B (130 words) - 08:16, 20 May 2013
  • 2008/09/30 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    1,015 B (153 words) - 08:15, 20 May 2013
  • Midterm Page MA375Fall2008walther
    [[Category:lecture notes]]
    7 KB (1,186 words) - 08:29, 20 May 2013
  • 2008/10/24 MA375Fall2008walther
    [[Category:lecture notes]]
    5 KB (756 words) - 08:23, 20 May 2013
  • 2008/10/23 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    1,019 B (147 words) - 08:15, 20 May 2013
  • Final Exam Page MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    3 KB (413 words) - 08:14, 20 May 2013
  • Midterm solutions MA375Fall2008walther
    [[Category:lecture notes]]
    6 KB (1,070 words) - 08:25, 20 May 2013
  • 2008/10/30 MA375Fall2008walther
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
    2 KB (381 words) - 08:14, 20 May 2013
  • N E W S MA375Fall2008walther
    Remember, Tuesday's (before thanksgiving) lecture is optional and we will be discussing interesting problems which will not b
    1 KB (172 words) - 10:56, 20 November 2008
  • Math Club Lecture TODAY MA375Fall2008walther
    Math Club lecture
    180 B (29 words) - 06:55, 11 December 2008
  • Lecture2 ECE301Fall2008mboutin
    In this lecture we deal on Continuous signals, Linearity, and of course, some MATLAB. Did the second lecture make sense? Please write your feedback here.
    2 KB (311 words) - 16:27, 3 December 2008
  • Lecture4 ECE301Fall2008mboutin
    =Course Notes for lecture 4, [[Main_Page_ECE301Fall2008mboutin|ECE301 Fall 2008, Prof. Boutin]]=
    1 KB (265 words) - 06:12, 2 February 2011
  • HW1.4 Miles Whittaker - Periodic and Non-Periodic Examples ECE301Fall2008mboutin
    Lecture Notes
    791 B (117 words) - 07:17, 14 April 2010
  • HW1.5 Miles Whittaker - Energy and Power ECE301Fall2008mboutin
    <font size="3">Lecture Notes</font>
    572 B (80 words) - 13:47, 4 September 2008
  • HW2.D Daniel Barjum ECE301Fall2008mboutin
    ...nd, shows up 10 minutes late to lecture, he will have missed 10 minutes of lecture (signal). Therefore, ECE 301 lectures are a "time-variant" system.
    1 KB (204 words) - 16:51, 12 September 2008
  • HW3.A Hetong Li ECE301Fall2008mboutin
    --Personally I think it is, but it somehow violates the definition given in lecture. Since it doesn't even depend on <math>t_0\,</math>)
    751 B (134 words) - 18:17, 17 September 2008
  • Lecture17 ECE301Fall2008mboutin
    [[Media:Lecture17_ECE301Fall2008mboutin.pdf|Lecture 17]] PDF requires Adobe Reader 7 or greater
    3 KB (449 words) - 17:07, 8 October 2008
  • Lecture19 ECE301Fall2008mboutin
    [[Media:Lecture19_ECE301Fall2008mboutin.pdf|Lecture 19]] PDF requires Adobe Reader 7 or greater
    103 B (14 words) - 12:48, 15 October 2008
  • Max Paganini: Properties of Region of Convergence (ROC) ECE301Fall2008mboutin
    Properties come from Mimi's Lecture #30.
    648 B (110 words) - 17:16, 17 November 2008
  • NotesWeek1 MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    589 B (86 words) - 09:59, 21 March 2013
  • NotesWeek3 MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    498 B (71 words) - 09:59, 21 March 2013
  • NotesWeek4 MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    817 B (125 words) - 10:02, 21 March 2013
  • NotesWeek4Th MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walth
    629 B (93 words) - 10:03, 21 March 2013
  • NotesWeek5Tues MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walth
    761 B (112 words) - 10:04, 21 March 2013
  • Possible discussion group on wednesdays... MA453Fall2008walther
    ...t week. I think Wed. night would be good and I bet we could find an empty lecture hall somewhere on campus.
    3 KB (534 words) - 12:35, 16 December 2008
  • NotesWeek7Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    458 B (65 words) - 10:05, 21 March 2013
  • Ch13-41 MA453Fall2008walther
    ...chapter. Could someone enlighten me on this question? I missed one of the lecture.<br>
    609 B (137 words) - 05:07, 30 October 2008
  • NotesWeek10Tues MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    590 B (88 words) - 10:06, 21 March 2013
  • NotesWeek9Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    524 B (72 words) - 10:06, 21 March 2013
  • NotesWeek11Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    547 B (79 words) - 10:07, 21 March 2013
  • NotesWeek12Tues MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    577 B (75 words) - 10:07, 21 March 2013
  • NotesWeek12Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    343 B (47 words) - 10:08, 21 March 2013
  • NotesWeek13Tues MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    504 B (72 words) - 10:09, 21 March 2013
  • NotesWeek13Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    512 B (72 words) - 10:09, 21 March 2013
  • NotesWeek15Thurs MA453Fall2008walther
    [[Category:lecture notes]] Lecture notes for [[MA453]]: "Abstract Algebra", Fall 2008, Prof. Walther
    436 B (55 words) - 10:10, 21 March 2013
  • Math Club Lecture TODAY MA453Fall2008walther
    Math Club lecture
    108 B (17 words) - 09:46, 9 December 2008
  • ECE662:BoutinSpring08 Old Kiwi
    ==Class Lecture Notes== * [[Lecture 1 - Introduction_Old Kiwi]]
    6 KB (747 words) - 05:18, 5 April 2013
  • ECE662:Glossary Old Kiwi
    ...ns of the two classes while minimizing the variance within each class. See Lecture 10 for detailed explanation. [http://www.csd.uwo.ca/~olga/Courses//CS434a_541a//Lecture8.pdf The Lecture and Example of Fisher Linear Discriminant]
    31 KB (4,832 words) - 18:13, 22 October 2010
  • ECE662 talk:BoutinSpring08 Old Kiwi
    ...res that we used on the old Kiwi, e.g. [[Lecture 2_Old Kiwi]] instead of [[Lecture 2 - Decision Hypersurfaces_Old Kiwi]]. --[[User:Yoder2|Yoder2]] 17:06, 6 Ma
    367 B (62 words) - 11:44, 8 May 2008
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi
    =Lecture 17, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
    6 KB (938 words) - 08:38, 17 January 2013
  • Lecture 1 - Introduction Old Kiwi
    =Lecture 1, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
    3 KB (468 words) - 08:45, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces Old Kiwi
    =Lecture 2, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
    5 KB (737 words) - 08:45, 17 January 2013
  • 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)
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  • Lecture 3 Old Kiwi
    #REDIRECT: [[Lecture 3 - Bayes classification_Old Kiwi]]
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  • Lecture 4 Old Kiwi
    #REDIRECT:[[Lecture 4 - Bayes Classification_Old Kiwi]]
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  • Lecture 4 - Bayes Classification Old Kiwi
    =Lecture 4, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • Decision Surfaces Old Kiwi
    (From [[Lecture 1 - Introduction_Old Kiwi]])
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  • Lecture 7 Old Kiwi
    #redirect: [[Lecture 7 - MLE and BPE_Old Kiwi]]
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  • Lecture 8 Old Kiwi
    #redirect:[[Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi]]
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  • Bayes Decision Rule Old Kiwi
    [[Category:lecture notes]] for [[Lecture_6_-_Discriminant_Functions_OldKiwi|Lecture 6, ECE662, Spring 2010]]
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  • 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
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  • Fisher Linear Discriminant Old Kiwi
    ...of the two classes while minimizing the variance within each class. See [[Lecture 10_Old Kiwi]] for detailed explanation.
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  • Lecture 11 Old Kiwi
    #redirect: [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi]]
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  • 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
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  • Perceptron Old Kiwi
    ...in [[Lecture_10_-_Batch_Perceptron_and_Fisher_Linear_Discriminant_OldKiwi|Lecture 10]].
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  • 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]]
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  • 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
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  • References Old Kiwi
    * [[Lecture Notes_Old Kiwi]]
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  • Curse of Dimensionality Old Kiwi
    [[Category:lecture notes]] from [[Lecture_2_-_Decision_Hypersurfaces_OldKiwi|Lecture 2, ECE662, Spring 2010]]
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  • 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
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  • Lecture 21 - Decision Trees(Continued) Old Kiwi
    =Lecture 21, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • 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
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  • Maximum A Posteriori Old Kiwi
    ...with Bayesian probability theory, but it works reasonably well. (See Also: Lecture 7 and [BPE]?)
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  • 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
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  • Lecture 23 - Spanning Trees Old Kiwi
    =Lecture 23, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • 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
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  • Lecture 25 - Clustering Algorithms Old Kiwi
    =Lecture 25, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • MLE Examples: Binomial and Poisson 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
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  • Parametric Classifiers Old Kiwi
    ...s to approximate true decision boundaries between classes. (Discussed in [[Lecture 9 - Linear Discriminant Functions_Old Kiwi]])
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  • Bayes classification Old Kiwi
    * [[Lecture 3 - Bayes classification_Old Kiwi]] * [[Lecture 4 - Bayes Classification_Old Kiwi]]
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  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    =Lecture 26, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    =Lecture 27, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • Lecture 28 - Final lecture Old Kiwi
    =Lecture 28, [[ECE662]]: Decision Theory= Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[us
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  • Solving differential equations iteratively Old Kiwi
    It may be useful to paste some material from [[Lecture 28 - Final lecture_Old Kiwi| here]]
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  • Laplacian Eigenmaps Old Kiwi
    * Spectral Graph Theory and its Applications, Lecture notes from Prof. Dan Spielman at Yale, 2004 [[http://www.cs.yale.edu/homes/
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  • ECE301:BoutinFall07 Old Kiwi
    Lecture: Tu, Th 15:00-16:15 @ GRIS180 ==Lecture Notes==
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  • ECE301:SanSummer08 Old Kiwi
    Lecture: M T W Th F 9:50 am - 10:50 am @ EE 117 #Lecture 1
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  • ECE301:SanSummer08:Exam II Old Kiwi
    Lecture 15 pg. 18 gives details of the properties for these types of functions.
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  • IdeasForInstructors Old Kiwi
    ...terial. Students in ECE301 Fall 2007 took pictures of the board during the lecture and posted them [https://engineering.purdue.edu/people/mireille.boutin.1/EC
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  • ECE302:SanghaviFall08 Old Kiwi
    Lecture: MWF 1:30-2:20PM @ PHYS 203 ==Lecture Notes==
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  • Course Summary ECE438Fall09mboutin
    .... 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]
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  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Category:lecture notes]] =Lecture 2 Lecture notes=
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  • ECE662:BoutinSpring08 OldKiwi
    ==Class Lecture Notes== * [[Lecture 1 - Introduction_OldKiwi|Lecture 1 - Introduction]]
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  • Hw3ECE438F09boutin
    [[Lecture_1%2C_8/24/2009_%28ECE_438_Fall09%29|Lecture 1, 8/24/2009 Notes]] --[[User:Rodrigaa|Rodrigaa]] 13:40, 21 September 2009 [https://kiwi.ecn.purdue.edu/rhea/index.php/ECE_438:_Lecture_09/11/2009 Lecture on 11Sep09] -- [[User:ssuresh|ssuresh]]
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  • Student summary Discrete Fourier transform ECE438F09
    ...in [[ECE_438_Fall_2009_mboutin_DFT_lecture_material|Some material for the lecture on DFT (re:leakage effect)]].
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  • Lectures
    [[Lecture 1]]
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  • Lecture 1
    [[Category:ECE301]] [[Category:Programming]] [[Category:Matlab]] [[Category:lecture notes]] =Lecture 1, [[ECE301]], Summer 2009, Landis Huffman=
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  • Exams/Quizzes
    ...all material through lecture on Wednesday, July 8. Thursday and Friday's lecture will review exam material. The topics include
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  • MA598R (WeigelSummer2009)
    * [[MA 598R pweigel Summer 2009 Lecture 1|Problem Set 1 - Review of MA 504]] * [[MA 598R pweigel Summer 2009 Lecture 2|Problem Set 2 - Functions of Bounded Variation and Measures]]
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  • MA 598R pweigel Summer 2009 Lecture 1
    =Lecture 1, MA598R, Weigel, Summer 2009=
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  • MA 598R pweigel Summer 2009 Lecture 2
    =Lecture 2, MA598R, Weigel, Summer 2009=
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  • MA 598R pweigel Summer 2009 Lecture 3
    =Lecture 3, MA598R, Weigel, Summer 2009=
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  • MA 598R pweigel Summer 2009 Lecture 4
    =Lecture 4, MA598, Summer 2009, Weigel=
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  • MA460 (Fall2009Walther)
    ...is the lowest level that you could take. This is going to be in a larger lecture hall and with little to no individual attention. Then you have 172 which i
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  • MA 598R pweigel Summer 2009 Lecture 5
    =Lecture 5, MA598R, Weigel, Summer 2009=
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  • MA 598R pweigel Summer 2009 Lecture 6
    =Lecture 6, MA598R, Weigel, Summer 2009=
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  • Final Cheat Sheet
    * [[HILBERT TRANSFORM - Lecture notes ]]
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  • ECE301 (BoumanFall2009)
    * Lecture Schedule:MWF 10:30-11:20AM in EE 270 == Lecture Notes ==
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  • MA 598R pweigel Summer 2009 Lecture 7
    =Lecture 7, MA598R, Weigel, Summer 2009=
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  • The Pirate's Booty
    Dead astern to [[MA_598R_pweigel_Summer_2009_Lecture_7|Lecture 7]]
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  • Aliasing
    Video lecture: [[Aliasing - Aaron Anderson]]<br />
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  • ECE 438 Fall 2009 mboutin Course Notes
    *[[Lecture_1%2C_8/24/2009_%28ECE_438_Fall09%29|Lecture 1, 8/24/2009]] *[[Rep_and_Comb_functions_:_Lecture_on_26/09/09|Lecture 2, 8/26/2009]]
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  • ECE438
    [[ECE438_digital_signal_processing_lecture_notes|Lecture Notes]]
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  • Detailed Course Description
    ===Lecture:=== ...in the text. On questions of terminology, definitions, and notation, your lecture notes should be relied upon, not the text.
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  • Tips for Exam Preparation
    ...nd the posted notes carefully. Make sure that you fully understand all the lecture material.
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  • ECE662
    == Slectures and Lecture Notes ==
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  • Graduate Studies in Mathematics
    ...-Yung Alice Chang (Princeton University) to deliver Jean E. Rubin Memorial Lecture as part of Women in Mathematics Day, 2009]]
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  • Important Links
    ...ECE462 home page in case there is anyone who doesn't know it. Go here for lecture videos and slides as well as important dates.
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  • Test
    ...CE_662_Boutin_Statistical_Pattern_recognition_slectures|ECE662 Spring 2014 lecture]] material of [[User:Mboutin|Prof. Mireille Boutin]]. </font> The materials given in this lecture are based on the lecture notes and the discussions that were shared in Prof. Boutin's ECE662 Spring
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  • RheaUsesShortPresentationAugust09
    **Example: [[Image_filtering_material_(ECE438BoutinSpring2009)|lecture with many complicated plots from ECE438]]
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  • Peer Legacy ECE302
    ...r equation and how different probabilistic situations differ, etc. I found lecture to be very minimalist (at least as far as going through practice problems), .... This course is one that is very applicable to future jobs and attending lecture and taking notes is very important. Studying and understanding the notes a
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  • ECE302
    [[ECE302_probability_lecture_notes|Lecture Notes]]
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  • 2009 Elementary Complex Analysis MA 425 Steve Bell
    [http://www.math.purdue.edu/~bell/MA425/Lectures Bell's tablet PC lecture notes]
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  • Lecture 9 - Linear Discriminant Functions OldKiwi
    [[Category:lecture notes]] =Lecture 9 Lecture notes=
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  • Lecture Notes 9-9-09
    Lecture Notes 9/9/2009: Z-1 of Rational Functions and Sampling
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  • Notes on Discrete Fourier Transform
    From [[ECE301|ECE301:"Signals and Systems"]] lecture by [[user:mboutin|Prof. Boutin]], Fall 2009.
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  • ECE 438: Lecture 09/11/2009
    == LECTURE on September 11, 2009 ==
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  • ECE 438 Fall 2009 mboutin DFT lecture material
    ...8]] Fall_2009 [[user:mboutin|Prof. Boutin]]: some graphs to illustrate DFT lecture=
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  • HWK5MA425Fall2009
    Also Prof Bell, If you do read this could you please post the lecture notes from last class online.
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  • HWK6MA425Fall2009
    [http://www.math.purdue.edu/~bell/MA425/Lectures/lec10-16.pdf Lecture 10/16/2009] --[[User:Bell|Steve Bell]]
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  • AliceChangTalkAnnouncementOct09
    ...delivers the scientific highlight of the day, the Jean E. Rubin Memorial Lecture, to the entire Mathematics Department in the afternoon. This year, Profess
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  • Student summary speech
    [[Category:lecture notes]] ==Post Lecture Speech notes. [[ECE438]], Fall 2009, [[user:mboutin|Prof. Boutin]]==
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  • SupplementarySpeech prelecture
    =Supplementary Speech Pre-lecture ([[ECE438]])= Due to a kiwi server fail, my pre-lecture notes are not as substantial as I would have liked
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  • ECE 438 Fall 2009 mboutin basic2Dfunctions
    This material is for the 11-04-09 lecture.
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  • 11-09 lecture note
    Partial ECE438 course notes for lecture on 11/09/2009
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  • 11-11 lecture note
    Lecture notes for class taught on November 11, 2009.
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  • SupplementalSpeech prelecture
    ...pre-lecture notes are not as substantial as I would have liked See my post-lecture notes for a more detailed description. Notes for speech lecture:
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  • HWK9MA425Fall2009
    ...lved in lecture (after the question was posted) so refer to the December 2 lecture for clarification on the notation and how to solve it. --[[User:Whoskins|Wh
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  • Audience awareness in posting
    ...users are students that read/post for course purposes such as homework or lecture review. I would hope that professors utilize RHEA to determine if student
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  • Peer Legacy ECE301
    ...to your own advantage. Lastly, if you aren't following the material during lecture, be sure to get help by either attending office hours with your professor o ...s you take notes in the class. Prof. Boumann also posted almost all of the lecture material online which is very helpful. --[[User: hirawa|hirawan]]
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  • Peer Legacy ECE438
    ...s still a good reference and useful to keep. There were a few times during lecture that the math became a little "hairy" and I wasn't able to follow step-by-s ...ECE438 lab is based on Matlab. Lastly, if you do homework and attend every lecture classes, I am pretty sure that you are doing well in ECE438.---[[User:sje|s
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  • Peer Legacy ECE201
    ...cooperative education student, I've used the principles taught in both the lecture and the lab time and time again. Take the time to understand it. It'll be
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  • Peer Legacy ECE311
    ...xplain material very detail and give some examples. It you read Dan Jiao's lecture notes carefully, you do not need to read the text book. [[User:Sje|sje]]
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  • MA453
    ==Lecture Notes (Prof. Walther-2008)==
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  • Image Processing Recitation
    ...ated. The material covered in this page covers topics presented in ECE438 lecture from 16-30 November. This page attempts to present a very general overview Several types of boundary conditions were discussed in lecture:
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  • MA375
    [[MA375_discrete_math_lecture_notes|Lecture Notes]]
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  • 2010 Spring ECE 662 mboutin
    ==Lecture Summaries== [[Lecture1ECE662S10|Lecture 1]],
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  • 1/12/10 Induction
    [[Category:lecture notes]] =[[MA375]]: Lecture Notes=
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  • 2010 Spring MA 530 Bell
    [http://www.math.purdue.edu/~bell/MA530/Lectures Bell's tablet PC lecture notes]
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  • HomeworkDiscussionsMA341Spring2010
    ...get a good grasp of the material by simply attending the lectures. On the lecture, I am giving you only a sketch of the topic, trying to give the sense of th
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  • MA341 HW discussion Section 2 2
    ...hould help to prepare questions. I will be happy to answer them during the lecture.
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  • MA 265 Spring 2010 Walther Course Notes
    == Lecture 4 (01/21/10) ==
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  • Star feedbackECE662S2010
    ...[ECE662Sp10_MakeupLectureNotes01|the lecture notes ]] of the first make up lecture
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  • OutlineECE662S10
    ! scope="col" | Lecture
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  • EE662Sp10Semimetric
    == Numeric Version of Example Given in Lecture == so here is the same example [[User:mboutin|Dr. Boutin]] gave in lecture of a
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  • EE662Sp10SymmetricGroup
    in lecture, is a group of permutation mappings on an
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  • ECE662Sp10 MakeupLectureNotes01
    ...for [[ 2010 Spring ECE 662 mboutin|ECE662 Spring 2010]] Lecture 21 (Makeup Lecture #1)=
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  • Lecture1ECE662S10
    =Details of Lecture 1, [[ECE662]] Spring 2010= In the first lecture, we covered the [[Media:SyllabusECE662S10.pdf|syllabus]] and gave a short i
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  • What is Pattern Recognition OldKiwi
    [[Category:lecture notes]] from [[Lecture_1_-_Introduction_OldKiwi|Lecture 1, ECE662, Spring 2010]]
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  • Lecture2ECE662S10
    =Details of Lecture 2, [[ECE662]] Spring 2010= In the second lecture, we discussed the difference between finite and infinite feature spaces. We
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  • Lecture3ECE662S10
    =Details of Lecture 3, [[ECE662]] Spring 2010= In Lecture 3, we continued our discussion of the user of hypersurfaces to separate the
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  • Lecture4ECE662S10
    =Details of Lecture 4, [[ECE662]] Spring 2010= In Lecture 4, we introduced two methods for finding decision hypersurfaces, namely: 1)
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  • Lecture5ECE662S10
    =Details of Lecture 5, [[ECE662]] Spring 2010= In Lecture 5, we observed that [[Bayes_Decision_Theory|Bayes rule]] minimizes the prob
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  • Lecture6ECE662S10
    =Details of Lecture 6, [[ECE662]] Spring 2010= In Lecture 6, we discussed [[Discriminant_Functions|discriminant functions]] and their
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  • Lecture21ECE662S10
    =Details of Lecture 21, [[ECE662]] Spring 2010= (see [[ECE662Sp10_MakeupLectureNotes01|lecture notes]])
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  • Lecture22ECE662S10
    =Details of Lecture 22, [[ECE662]] Spring 2010, [[User:mboutin|Prof. Boutin]]= In Lecture 22, we continued our discussion of [[Fisher Linear Discriminant|Fisher's li
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  • Lecture23ECE662S10
    =Details of Lecture 23, [[ECE662]] Spring 2010= In Lecture 23, we continued discussing [[Support_Vector_Machines|Support Vector Machin
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  • Support Vector Machines
    *ECE662, Spring 2010, [[User:mboutin|Prof. Boutin]]: Lecture [[Lecture22ECE662S10|22]], [[Lecture23ECE662S10|23]] *ECE662, Spring 2008, [[User:mboutin|Prof. Boutin]]: Lecture [[Lecture_11_-_Fischer's_Linear_Discriminant_again_OldKiwi|11]],[[Lecture_1
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  • Feature Extraction
    *ECE662, Spring 2010, Prof. Boutin [[Lecture21ECE662S10|Lecture 21 on Perceptron, Least squares formulation of linear discriminant, and Fis *ECE662, Spring 2010, Prof. Boutin [[Lecture22ECE662S10|Lecture 22 on Fisher linear discriminant]]
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  • Fisher Linear Discriminant
    *ECE662, Spring 2010, Prof. Boutin [[Lecture21ECE662S10|Lecture 21 on Perceptron, Least squares formulation of linear discriminant, and Fis *ECE662, Spring 2010, Prof. Boutin [[Lecture22ECE662S10|Lecture 22 on Fisher linear discriminant]]
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  • Bayes Decision Theory
    *ECE662 Spring 2010, Prof. Boutin [[Lecture4ECE662S10|Lecture 4]] *ECE662 Spring 2010, Prof. Boutin [[Lecture5ECE662S10|Lecture 5]]
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  • Hw1periodicECE301f08profcomments
    ...it not periodic? This is one thing we will study in class. Don't miss the lecture!
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  • Lecture24ECE662S10
    =Details of Lecture 24, [[ECE662]] Spring 2010= <span style="color:green"> This is a make up lecture, 1:30-2:30 in EE117. </span>
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  • Lecture25ECE662S10
    =Details of Lecture 25, [[ECE662]] Spring 2010= In Lecture 25, we finished our discussion of [[Support_Vector_Machines|Support Vector
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  • Artificial Neural Networks OldKiwi
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    2 KB (296 words) - 10:46, 16 April 2010
  • Artificial Neural Networks
    *ECE662, Spring 2010, Prof. Boutin [[Lecture25ECE662S10|Lecture 25 on SVM and artificial neural networks]] *ECE662, Spring 2010, Prof. Boutin [[Lecture26ECE662S10|Lecture 26 on artificial neural networks]]
    1 KB (146 words) - 10:47, 16 April 2010

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Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett