| Line 26: | Line 26: | ||
== Discussions and Students' perspectives == | == Discussions and Students' perspectives == | ||
*[[ECE662_topic1_discussions|Week 1: Introduction and Expectations]] | *[[ECE662_topic1_discussions|Week 1: Introduction and Expectations]] | ||
| + | *[[ECE662_topic1_discussions|Week 3: Is Bayes truly the best?]] | ||
*[[EE662Sp10OptimalPrediction|Optimality of Always Predicting the Most Likely Outcome]] | *[[EE662Sp10OptimalPrediction|Optimality of Always Predicting the Most Likely Outcome]] | ||
Revision as of 06:50, 28 January 2010
Contents
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
- 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
Discussions and Students' perspectives
- Week 1: Introduction and Expectations
- Week 3: Is Bayes truly the best?
- Optimality of Always Predicting the Most Likely Outcome
Homework
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
