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| − | [ | + | =Lecture 1, [[ECE662]]: Decision Theory= |
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| + | Lecture notes for [[ECE662:BoutinSpring08_Old_Kiwi|ECE662 Spring 2008]], Prof. [[user:mboutin|Boutin]]. | ||
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| + | Other lectures: [[Lecture 1 - Introduction_Old Kiwi|1]], | ||
| + | [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]], | ||
| + | [[Lecture 3 - Bayes classification_Old Kiwi|3]], | ||
| + | [[Lecture 4 - Bayes Classification_Old Kiwi|4]], | ||
| + | [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], | ||
| + | [[Lecture 6 - Discriminant Functions_Old Kiwi|6]], | ||
| + | [[Lecture 7 - MLE and BPE_Old Kiwi|7]], | ||
| + | [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_Old Kiwi|8]], | ||
| + | [[Lecture 9 - Linear Discriminant Functions_Old Kiwi|9]], | ||
| + | [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_Old Kiwi|10]], | ||
| + | [[Lecture 11 - Fischer's Linear Discriminant again_Old Kiwi|11]], | ||
| + | [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]], | ||
| + | [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|13]], | ||
| + | [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_Old Kiwi|14]], | ||
| + | [[Lecture 15 - Parzen Window Method_Old Kiwi|15]], | ||
| + | [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_Old Kiwi|16]], | ||
| + | [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], | ||
| + | [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]], | ||
| + | [[Lecture 19 - Nearest Neighbor Error Rates_Old Kiwi|19]], | ||
| + | [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_Old Kiwi|20]], | ||
| + | [[Lecture 21 - Decision Trees(Continued)_Old Kiwi|21]], | ||
| + | [[Lecture 22 - Decision Trees and Clustering_Old Kiwi|22]], | ||
| + | [[Lecture 23 - Spanning Trees_Old Kiwi|23]], | ||
| + | [[Lecture 24 - Clustering and Hierarchical Clustering_Old Kiwi|24]], | ||
| + | [[Lecture 25 - Clustering Algorithms_Old Kiwi|25]], | ||
| + | [[Lecture 26 - Statistical Clustering Methods_Old Kiwi|26]], | ||
| + | [[Lecture 27 - Clustering by finding valleys of densities_Old Kiwi|27]], | ||
| + | [[Lecture 28 - Final lecture_Old Kiwi|28]], | ||
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Lecture Notes: | Lecture Notes: | ||
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A Variety is a mathematical construct used to define a decision surface. | A Variety is a mathematical construct used to define a decision surface. | ||
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Latest revision as of 08:45, 17 January 2013
Contents
Lecture 1, ECE662: Decision Theory
Lecture notes for ECE662 Spring 2008, Prof. Boutin.
Other lectures: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
Lecture Notes:
This was the first day of class. These notes are from the class lecture.
Links to Course Webpages
Login: Use your Purdue Career Account username and password.
Note: You must change your password once a month.
Kiwi Week
Monday at noon until Monday at noon.
Textbook Information
Main article: Textbooks_Old Kiwi
There is not a single book that covers all the things that will be discussed in ECE 662. The class will reference four books during the course of the semester as we cover various topics. All four of them are available through the reserves at the engineering library.
Definition and Examples of Pattern Recognition
Main article: What is Pattern Recognition_Old Kiwi.
Pattern Recognition is the art of assigning classes or categories to data.
Decision Surfaces
Main Article: Decision Surfaces_Old Kiwi
Decision surfaces are the boundaries in the feature space that distinguish classes.
Algebraic Geometry
Main Article: Decision Surfaces_Old Kiwi (This is not a typo)
Varieties
Main Article: Varieties_Old Kiwi
A Variety is a mathematical construct used to define a decision surface.
