| Line 20: | Line 20: | ||
*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, 1:30-2:30, EE117. | *Make up classes: Friday April 9, 16, 23, 1:30-2:30, EE117. | ||
| + | |||
| + | ==Lectures in more Details== | ||
| + | *[[Lecture1ECE662S10|Lecture 1]] | ||
| + | *[[Lecture2ECE662S10|Lecture 2]] | ||
| + | *[[Lecture3ECE662S10|Lecture 3]] | ||
| + | *[[Lecture4ECE662S10|Lecture 4]] | ||
| + | *[[Lecture5ECE662S10|Lecture 5]] | ||
| + | *[[Lecture6ECE662S10|Lecture 6]] | ||
| + | *[[Lecture7ECE662S10|Lecture 7]] | ||
| + | *[[Lecture8ECE662S10|Lecture 8]] | ||
| + | *[[Lecture9ECE662S10|Lecture 9]] | ||
| + | *[[Lecture10ECE662S10|Lecture 10]] | ||
| + | *[[Lecture11ECE662S10|Lecture 11]] | ||
| + | *[[Lecture12ECE662S10|Lecture 12]] | ||
| + | *[[Lecture13ECE662S10|Lecture 13]] | ||
| + | *[[Lecture14ECE662S10|Lecture 14]] | ||
| + | *[[Lecture15ECE662S10|Lecture 15]] | ||
| + | *[[Lecture16ECE662S10|Lecture 16]] | ||
| + | *[[Lecture17ECE662S10|Lecture 17]] | ||
| + | *[[Lecture18ECE662S10|Lecture 18]] | ||
| + | *[[Lecture19ECE662S10|Lecture 19]] | ||
| + | *[[Lecture20ECE662S10|Lecture 20]] | ||
| + | *[[Lecture21ECE662S10|Lecture 21]] | ||
| + | *[[Lecture22ECE662S10|Lecture 22]] | ||
| + | *[[Lecture23ECE662S10|Lecture 23]] | ||
| + | *[[Lecture24ECE662S10|Lecture 24]] | ||
| + | *[[Lecture25ECE662S10|Lecture 25]] | ||
| + | *[[Lecture26ECE662S10|Lecture 26]] | ||
| + | *[[Lecture27ECE662S10|Lecture 27]] | ||
| + | *[[Lecture28ECE662S10|Lecture 28]] | ||
| + | *[[Lecture29ECE662S10|Lecture 29]] | ||
| + | *[[Lecture30ECE662S10|Lecture 30]] | ||
== Links and Material Used in Class == | == Links and Material Used in Class == | ||
Revision as of 06:33, 12 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.
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 first hw here.
- Hw3: Discuss the first hw here.
- Linear Perceptron classifier in Batch mode
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
