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*Make up classes: Friday April 9, 16, 23, 30, 1:30-2:30, EE117. | *Make up classes: Friday April 9, 16, 23, 30, 1:30-2:30, EE117. | ||
| − | == | + | ==Lecture Summaries== |
[[Lecture1ECE662S10|Lecture 1]], | [[Lecture1ECE662S10|Lecture 1]], | ||
[[Lecture2ECE662S10|2]], | [[Lecture2ECE662S10|2]], | ||
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,[[Lecture27ECE662S10|27]] | ,[[Lecture27ECE662S10|27]] | ||
| − | ,[[Lecture28ECE662S10|28]]. | + | ,[[Lecture28ECE662S10|28]] |
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== Links and Material Used in Class == | == Links and Material Used in Class == | ||
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*[[One_class_svm|One-Class Support Vector Machines for Anomaly Detection]] | *[[One_class_svm|One-Class Support Vector Machines for Anomaly Detection]] | ||
*[[EE662Sp10_HiddenMarkovModel|Intro to Hidden Markov Model]] | *[[EE662Sp10_HiddenMarkovModel|Intro to Hidden Markov Model]] | ||
| + | *[[ANN_Simulink_examples_ece662_Sp2010|ANN Jump Start: Using MATLAB Simulink to train a network]] | ||
== Feedback == | == Feedback == | ||
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== Class Notes == | == Class Notes == | ||
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*[[ECE662Sp10_MakeupLectureNotes01|Makeup Lecture #1, 9 April 2010]] | *[[ECE662Sp10_MakeupLectureNotes01|Makeup Lecture #1, 9 April 2010]] | ||
| − | + | *[[Noteslecture8ECE662S10|Lecture 8]] | |
| + | *[[Noteslecture11ECE662S10|Lecture 11]] | ||
| + | *[[Noteslecture20ECE662S10|Lecture 20, Thursday April 8, 2010]] | ||
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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
