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| − | + | = [[ECE662]]: "Statistical Pattern Recognition and Decision Making Processes", Spring 2010 = | |
| − | =[[ECE662]]: " | + | <div style="border-style: solid; border-color: rgb(68, 68, 136) rgb(68, 68, 136) rgb(68, 68, 136) rgb(51, 51, 136); border-width: 1px 1px 1px 4px; margin: auto; padding: 2em; background: rgb(238, 238, 255) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial; width: 30em; text-align: center;"> |
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Message Area: | Message Area: | ||
| − | + | 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"]] | |
| + | </div> | ||
| + | == General Course Information == | ||
| − | + | *Instructor: [[User:Mboutin|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 [[Open office hours mboutin|here]] | ||
| + | *[[Media:SyllabusECE662S10.pdf|Syllabus]] | ||
| + | *[[OutlineECE662S10|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== | |
| + | [[Lecture1ECE662S10|Lecture 1]], | ||
| + | [[Lecture2ECE662S10|2]], | ||
| + | [[Lecture3ECE662S10|3]] | ||
| + | ,[[Lecture4ECE662S10|4]] | ||
| + | ,[[Lecture5ECE662S10|5]] | ||
| + | ,[[Lecture6ECE662S10|6]] | ||
| + | ,[[Lecture7ECE662S10|7]] | ||
| + | ,[[Lecture8ECE662S10|8]] | ||
| + | ,[[Lecture9ECE662S10|9]] | ||
| + | ,[[Lecture10ECE662S10|10]] | ||
| + | ,[[Lecture11ECE662S10|11]] | ||
| + | ,[[Lecture12ECE662S10|12]] | ||
| + | ,[[Lecture13ECE662S10|13]] | ||
| + | ,[[Lecture14ECE662S10|14]] | ||
| + | ,[[Lecture15ECE662S10|15]] | ||
| + | ,[[Lecture16ECE662S10|16]] | ||
| + | ,[[Lecture17ECE662S10|17]] | ||
| + | ,[[Lecture18ECE662S10|18]] | ||
| + | ,[[Lecture19ECE662S10|19]] | ||
| + | ,[[Lecture20ECE662S10|20]] | ||
| + | ,[[Lecture21ECE662S10|21]] | ||
| + | ,[[Lecture22ECE662S10|22]] | ||
| + | ,[[Lecture23ECE662S10|23]] | ||
| + | ,[[Lecture24ECE662S10|24]] | ||
| + | ,[[Lecture25ECE662S10|25]] | ||
| + | ,[[Lecture26ECE662S10|26]] | ||
| + | ,[[Lecture27ECE662S10|27]] | ||
| + | ,[[Lecture28ECE662S10|28]] | ||
| + | ,[[Lecture29ECE662S10|29]]. | ||
| − | == | + | == Links and Material Used in Class == |
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*[http://www.statisticalengineering.com/central_limit_theorem.htm Illustration of Central Limit Theorem with uniform distrribution] | *[http://www.statisticalengineering.com/central_limit_theorem.htm Illustration of Central Limit Theorem with uniform distrribution] | ||
| − | == Discussions and Students' perspectives == | + | == Discussions and Students' perspectives == |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | *[[ECE662 topic1 discussions|Introduction and Expectations]] | |
| − | * [[ | + | *[[ECE662 topic2 discussions|Is Bayes truly the best?]] |
| − | *[[ | + | *[[ECE662 topic3 discussions|Central Limit Theorem illustrations]] |
| + | *[[ECE662 hw1 discussions|Hw1: Discuss the first hw here.]] | ||
| + | *[[EE662Sp10Semimetric|Distance Functions Where Triangle Inequality Doesn't Hold]] | ||
| + | *[[EE662Sp10AbstarctAlgebra|Group Theory Background for 3-25-10 and 3-30-10 Lectures]] | ||
| + | *[[ECE662 hw2 discussions|Hw2: Discuss the second hw here.]] | ||
| + | *[[ECE662 hw3 discussions|Hw3: Discuss the third hw here.]] | ||
| + | *[[ECE662 topic8 discussions|Linear Perceptron classifier in Batch mode]] | ||
| + | *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] | ||
| + | *[[Fisher_discriminant_under_nonlinear_data|Fisher linear discriminant in non linearly separable data]] | ||
| + | *[[One_class_svm|One-Class Support Vector Machines for Anomaly Detection]] | ||
| + | *[[EE662Sp10_HiddenMarkovModel|Intro to Hidden Markov Model]] | ||
| + | *[[ANN_Simulink_examples_ece662_Sp2010|ANN Jump Start: Using MATLAB Simulink to train a network]] | ||
| − | == | + | == Feedback == |
| − | * [[ | + | *[[Star feedbackECE662S2010|Stars for Rhea participation]] <span style="text-decoration: blink;"> New! </span> |
| − | + | *[[FavoritedecisionECE662S10|Student Poll: What is your favorite decision method?]] | |
| − | * [[ | + | |
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| + | == Homework == | ||
| + | |||
| + | *[[Hw0 ECE662Spring2010|HW0 - getting ready]] | ||
| + | *[[Hw1 ECE662Spring2010|HW1- Bayes rule for normally distributed features]] | ||
| + | *[[Hw2 ECE662Spring2010|HW2- Bayes rule using parametric density estimation]] | ||
| + | *[[Hw3 ECE662Spring2010|HW3- Bayes rule using non-parametric density estimation]] | ||
| + | |||
| + | == References == | ||
| + | |||
| + | *[["Introduction to Statistical Pattern Recognition" by K. Fukunaga OldKiwi]] (This is the main reference) | ||
| + | *[["Pattern Classification" by Duda, Hart, and Stork OldKiwi]] | ||
| + | *[["Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler OldKiwi]] | ||
| + | *[["Pattern Recognition and Neural Networks" by Brian Ripley OldKiwi]] | ||
| + | *[["Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar OldKiwi]] | ||
| + | |||
| + | == Class Notes == | ||
| + | *[[ECE662Sp10_MakeupLectureNotes01|Makeup Lecture #1, 9 April 2010]] | ||
| + | *[[Noteslecture8ECE662S10|Lecture 8]] | ||
| + | *[[Noteslecture11ECE662S10|Lecture 11]] | ||
| + | *[[Noteslecture20ECE662S10|Lecture 20, Thursday April 8, 2010]] | ||
---- | ---- | ||
| − | [[Course List|Back to course list]] | + | |
| + | [[Course List|Back to course list]] | ||
| + | |||
| + | [[Category:ECE662Spring2010mboutin]] | ||
Latest revision as of 11:24, 25 June 2010
Contents
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
- Introduction and Expectations
- Is Bayes truly the best?
- Central Limit Theorem illustrations
- Hw1: Discuss the first hw here.
- Distance Functions Where Triangle Inequality Doesn't Hold
- Group Theory Background for 3-25-10 and 3-30-10 Lectures
- Hw2: Discuss the second hw here.
- Hw3: Discuss the third hw here.
- Linear Perceptron classifier in Batch mode
- Bayes rule under severe class imbalance
- Fisher linear discriminant in non linearly separable data
- One-Class Support Vector Machines for Anomaly Detection
- Intro to Hidden Markov Model
- ANN Jump Start: Using MATLAB Simulink to train a network
Feedback
Homework
- HW0 - getting ready
- HW1- Bayes rule for normally distributed features
- HW2- Bayes rule using parametric density estimation
- HW3- Bayes rule using non-parametric density estimation
References
- "Introduction to Statistical Pattern Recognition" by K. Fukunaga OldKiwi (This is the main reference)
- "Pattern Classification" by Duda, Hart, and Stork OldKiwi
- "Pattern Recognition: A Statistical Approach" by P.A. Devijver and J.V. Kittler OldKiwi
- "Pattern Recognition and Neural Networks" by Brian Ripley OldKiwi
- "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar OldKiwi
