Contents
ECE662: "Satistical Pattern Recognition and Decision Making Processes", Spring 2010
Message Area:
It appears that the login problem has been fixed. Ye!!!! Please make sure to catch up on your Rhea contributions now (look at the "stars" page to see how you are doing compared to your classmates.)
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: to be announced. Please list the times when you cannot meet here
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
