Revision as of 09:05, 11 April 2010

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

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

• Illustration of Central Limit Theorem with uniform distrribution

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.
• Linear Perceptron classifier in Batch mode

Feedback

• Stars for Rhea participation New!

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

Class Notes

• Makeup Lecture #1, 9 April 2010

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