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[[Category:ECE662Spring2010mboutin]]
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= [[ECE662]]: "Statistical Pattern Recognition and Decision Making Processes", Spring 2010 =
=[[ECE662]]: "Satistical Pattern Recognition and Decision Making Processes", Spring 2010=
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Message Area:
 
Message Area:
  
Welcome!
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If you are interested in robotics and vision, there is a new course for you next Fall: [[2010_Fall_IE_590_Wachs| IE 590 "Robotics and Machine Vision"]]
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== General Course Information ==
  
Rhea is now on [http://www.facebook.com/pages/West-Lafayette-IN/Rhea/88771959948 Facebook].
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*Instructor: [[User:Mboutin|Prof. Boutin]] a.k.a. Prof. Mimi
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*Office: MSEE342
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*Email: mboutin at purdue dot you know where
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*Class meets Tu,Th 12-13:15 in EE115
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*Office hours are listed [[Open office hours mboutin|here]]
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*[[Media:SyllabusECE662S10.pdf|Syllabus]]
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*[[OutlineECE662S10|Course Outline]]
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*Class cancellation: Jan 19, Jan 21, Feb 23, Feb 25
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*Make up classes: Friday April 9, 16, 23, 30, 1:30-2:30, EE117.
  
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==Lecture Summaries==
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[[Lecture1ECE662S10|Lecture 1]],
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[[Lecture2ECE662S10|2]],
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[[Lecture3ECE662S10|3]]
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,[[Lecture4ECE662S10|4]]
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,[[Lecture5ECE662S10|5]]
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,[[Lecture6ECE662S10|6]]
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,[[Lecture7ECE662S10|7]]
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,[[Lecture8ECE662S10|8]]
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,[[Lecture9ECE662S10|9]]
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,[[Lecture10ECE662S10|10]]
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,[[Lecture11ECE662S10|11]]
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,[[Lecture12ECE662S10|12]]
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,[[Lecture13ECE662S10|13]]
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,[[Lecture14ECE662S10|14]]
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,[[Lecture15ECE662S10|15]]
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,[[Lecture16ECE662S10|16]]
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,[[Lecture17ECE662S10|17]]
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,[[Lecture18ECE662S10|18]]
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,[[Lecture19ECE662S10|19]]
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,[[Lecture20ECE662S10|20]]
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,[[Lecture21ECE662S10|21]]
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,[[Lecture22ECE662S10|22]]
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,[[Lecture23ECE662S10|23]]
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,[[Lecture24ECE662S10|24]]
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,[[Lecture25ECE662S10|25]]
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,[[Lecture26ECE662S10|26]]
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,[[Lecture27ECE662S10|27]]
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,[[Lecture28ECE662S10|28]]
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,[[Lecture29ECE662S10|29]].
  
==General Course Information==
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== Links and Material Used in Class ==
* Instructor: [[User:mboutin|Prof. Boutin]] a.k.a. Prof. Mimi
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* Office: MSEE342
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* Email: mboutin at purdue dot you know where
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* Class meets Tu,Th 12-13:15 in EE115
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* Office hours are listed [[Open_office_hours_mboutin|here]]
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*[[Media:syllabusECE662S10.pdf|Syllabus]]
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* Class cancellation: Jan 19, Jan 21, Feb 23, Feb 25
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* Make up classes: to be announced. Please list the times when you cannot meet [[schedule_surveyECE662S09|here]]
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== Discussions and Students' perspectives ==
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*[http://www.statisticalengineering.com/central_limit_theorem.htm Illustration of Central Limit Theorem with uniform distrribution]
*[[ECE662_topic1_discussions|Week 1: Introduction and Expectations]]
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*[[ECE662_topic2_discussions|Week 3: Is Bayes truly the best?]]
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*[[EE662Sp10OptimalPrediction|Optimality of Always Predicting the Most Likely Outcome]]
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==Homework==
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== Discussions and Students' perspectives  ==
* [[hw0_ECE662Spring2010|HW0 - getting ready]]
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==References==
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*[[ECE662 topic1 discussions|Introduction and Expectations]]
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*[[ECE662 topic2 discussions|Is Bayes truly the best?]]
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*[[ECE662 topic3 discussions|Central Limit Theorem illustrations]]
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*[[ECE662 hw1 discussions|Hw1: Discuss the first hw here.]]
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*[[EE662Sp10Semimetric|Distance Functions Where Triangle Inequality Doesn't Hold]]
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*[[EE662Sp10AbstarctAlgebra|Group Theory Background for 3-25-10 and 3-30-10 Lectures]]
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*[[ECE662 hw2 discussions|Hw2: Discuss the second hw here.]]
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*[[ECE662 hw3 discussions|Hw3: Discuss the third hw here.]]
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*[[ECE662 topic8 discussions|Linear Perceptron classifier in Batch mode]]
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*[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]
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*[[Fisher_discriminant_under_nonlinear_data|Fisher linear discriminant in non linearly separable data]]
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*[[One_class_svm|One-Class Support Vector Machines for Anomaly Detection]]
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*[[EE662Sp10_HiddenMarkovModel|Intro to Hidden Markov Model]]
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*[[ANN_Simulink_examples_ece662_Sp2010|ANN Jump Start: Using MATLAB Simulink to train a network]]
  
* [["Introduction to Statistical Pattern Recognition" by K. Fukunaga_OldKiwi]] (This is the main reference)
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== Feedback  ==
* [["Pattern Classification" by Duda, Hart, and Stork_OldKiwi]]
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* [["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler_OldKiwi]]
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* [["Pattern Recognition and Neural Networks" by Brian Ripley_OldKiwi]]
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* [["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar_OldKiwi]]
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*[[Star feedbackECE662S2010|Stars for Rhea participation]] <span style="text-decoration: blink;"> New! </span>
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*[[FavoritedecisionECE662S10|Student Poll: What is your favorite decision method?]]
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== Homework ==
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*[[Hw0 ECE662Spring2010|HW0 - getting ready]]
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*[[Hw1 ECE662Spring2010|HW1- Bayes rule for normally distributed features]]
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*[[Hw2 ECE662Spring2010|HW2- Bayes rule using parametric density estimation]]
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*[[Hw3 ECE662Spring2010|HW3- Bayes rule using non-parametric density estimation]]
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== References ==
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*[["Introduction to Statistical Pattern Recognition" by K. Fukunaga OldKiwi]] (This is the main reference)
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*[["Pattern Classification" by Duda, Hart, and Stork OldKiwi]]
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*[["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler OldKiwi]]
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*[["Pattern Recognition and Neural Networks" by Brian Ripley OldKiwi]]
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*[["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar OldKiwi]]
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== Class Notes ==
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*[[ECE662Sp10_MakeupLectureNotes01|Makeup Lecture #1, 9 April 2010]]
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*[[Noteslecture8ECE662S10|Lecture 8]]
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*[[Noteslecture11ECE662S10|Lecture 11]]
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*[[Noteslecture20ECE662S10|Lecture 20, Thursday April 8, 2010]]
 
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[[Course List|Back to course list]]
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[[Course List|Back to course list]]
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[[Category:ECE662Spring2010mboutin]]

Latest revision as of 12:24, 25 June 2010



ECE662: "Statistical Pattern Recognition and Decision Making Processes", Spring 2010

Message Area:

If you are interested in robotics and vision, there is a new course for you next Fall: IE 590 "Robotics and Machine Vision"

General Course Information

  • Instructor: Prof. Boutin a.k.a. Prof. Mimi
  • Office: MSEE342
  • Email: mboutin at purdue dot you know where
  • Class meets Tu,Th 12-13:15 in EE115
  • Office hours are listed here
  • Syllabus
  • Course Outline
  • Class cancellation: Jan 19, Jan 21, Feb 23, Feb 25
  • Make up classes: Friday April 9, 16, 23, 30, 1:30-2:30, EE117.

Lecture Summaries

Lecture 1, 2, 3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 ,18 ,19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29.

Links and Material Used in Class

Discussions and Students' perspectives

Feedback

Homework

References

Class Notes


Back to course list

Alumni Liaison

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