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| − | + | The dates for the make classes are Friday April 9,16,23. Time is 1:30-2:30. Location EE117. | |
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== General Course Information == | == General Course Information == | ||
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*[[OutlineECE662S10|Course Outline]] | *[[OutlineECE662S10|Course Outline]] | ||
*Class cancellation: Jan 19, Jan 21, Feb 23, Feb 25 | *Class cancellation: Jan 19, Jan 21, Feb 23, Feb 25 | ||
| − | *Make up classes: Friday April 9, 16, 23 | + | *Make up classes: Friday April 9, 16, 23, 1:30-2:30, EE117. |
== Links and Material Used in Class == | == Links and Material Used in Class == | ||
Revision as of 07:33, 7 April 2010
Contents
ECE662: "Satistical Pattern Recognition and Decision Making Processes", Spring 2010
Message Area:
The dates for the make classes are Friday April 9,16,23. Time is 1:30-2:30. Location EE117.
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, 1:30-2:30, EE117.
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 first hw here.
- Hw3: Discuss the first hw here.
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
