| Line 6: | Line 6: | ||
<div style="border-style: solid; border-color: rgb(68, 68, 136) rgb(68, 68, 136) rgb(68, 68, 136) rgb(51, 51, 136); border-width: 1px 1px 1px 4px; margin: auto; padding: 2em; background: rgb(238, 238, 255) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial; width: 30em; text-align: center;"> | <div style="border-style: solid; border-color: rgb(68, 68, 136) rgb(68, 68, 136) rgb(68, 68, 136) rgb(51, 51, 136); border-width: 1px 1px 1px 4px; margin: auto; padding: 2em; background: rgb(238, 238, 255) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial; width: 30em; text-align: center;"> | ||
Message Area: | Message Area: | ||
| − | + | If you are interested in robotics and vision, there is a new course for you next Fall: [[2010_Fall_IE_590_Wachs| IE 590 "Robotics and Machine Vision"]] | |
| − | + | ||
</div> | </div> | ||
== General Course Information == | == General Course Information == | ||
Revision as of 14:39, 19 April 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, 1:30-2:30, EE117.
Lectures in more Details
- Lecture 1
- Lecture 2
- Lecture 3
- Lecture 4
- Lecture 5
- Lecture 6
- Lecture 7
- Lecture 8
- Lecture 9
- Lecture 10
- Lecture 11
- Lecture 12
- Lecture 13
- Lecture 14
- Lecture 15
- Lecture 16
- Lecture 17
- Lecture 18
- Lecture 19
- Lecture 20
- Lecture 21
- Lecture 22
- Lecture 23
- Lecture 24
- Lecture 25
- Lecture 26
- Lecture 27
- Lecture 28
- Lecture 29
- Lecture 30
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
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
