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| − | [[ECE662 | + | [[Category:ECE662]] |
| + | [[Category:decision theory]] | ||
| + | [[Category:lecture notes]] | ||
| + | [[Category:pattern recognition]] | ||
| + | [[Category:slecture]] | ||
| − | + | <center><font size= 4> | |
| − | + | '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' | |
| + | </font size> | ||
| − | + | Spring 2008, [[user:mboutin|Prof. Boutin]] | |
| − | + | [[Slectures|Slecture]] | |
| − | + | <font size= 3> Collectively created by the students in [[ECE662:BoutinSpring08_OldKiwi|the class]]</font size> | |
| + | </center> | ||
| − | + | ---- | |
| − | + | =Lecture 1 Lecture notes= | |
| − | ' | + | Jump to: [[ECE662_Pattern_Recognition_Decision_Making_Processes_Spring2008_sLecture_collective|Outline]]| |
| − | + | [[Lecture 1 - Introduction_OldKiwi|1]]| | |
| − | + | [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]| | |
| − | + | [[Lecture 3 - Bayes classification_OldKiwi|3]]| | |
| − | + | [[Lecture 4 - Bayes Classification_OldKiwi|4]]| | |
| + | [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| | ||
| + | [[Lecture 6 - Discriminant Functions_OldKiwi|6]]| | ||
| + | [[Lecture 7 - MLE and BPE_OldKiwi|7]]| | ||
| + | [[Lecture 8 - MLE, BPE and Linear Discriminant Functions_OldKiwi|8]]| | ||
| + | [[Lecture 9 - Linear Discriminant Functions_OldKiwi|9]]| | ||
| + | [[Lecture 10 - Batch Perceptron and Fisher Linear Discriminant_OldKiwi|10]]| | ||
| + | [[Lecture 11 - Fischer's Linear Discriminant again_OldKiwi|11]]| | ||
| + | [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| | ||
| + | [[Lecture 13 - Kernel function for SVMs and ANNs introduction_OldKiwi|13]]| | ||
| + | [[Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)_OldKiwi|14]]| | ||
| + | [[Lecture 15 - Parzen Window Method_OldKiwi|15]]| | ||
| + | [[Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate_OldKiwi|16]]| | ||
| + | [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| | ||
| + | [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]| | ||
| + | [[Lecture 19 - Nearest Neighbor Error Rates_OldKiwi|19]]| | ||
| + | [[Lecture 20 - Density Estimation using Series Expansion and Decision Trees_OldKiwi|20]]| | ||
| + | [[Lecture 21 - Decision Trees(Continued)_OldKiwi|21]]| | ||
| + | [[Lecture 22 - Decision Trees and Clustering_OldKiwi|22]]| | ||
| + | [[Lecture 23 - Spanning Trees_OldKiwi|23]]| | ||
| + | [[Lecture 24 - Clustering and Hierarchical Clustering_OldKiwi|24]]| | ||
| + | [[Lecture 25 - Clustering Algorithms_OldKiwi|25]]| | ||
| + | [[Lecture 26 - Statistical Clustering Methods_OldKiwi|26]]| | ||
| + | [[Lecture 27 - Clustering by finding valleys of densities_OldKiwi|27]]| | ||
| + | [[Lecture 28 - Final lecture_OldKiwi|28]] | ||
| + | ---- | ||
| + | ---- | ||
| + | This was the first day of class. These notes are from the class lecture. | ||
| + | ==Course Info== | ||
| + | [[Course_info_ECE662_Boutin_Spring2010|Continue reading...]] | ||
== Textbook Information == | == Textbook Information == | ||
| − | + | There is not a single book that covers all the things that will be discussed in ECE 662. The class will reference [[Textbooks_OldKiwi|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. | |
| − | + | ||
| − | There is not a single book that covers all the things that will be discussed in ECE 662. The class will reference [[ | + | |
== Definition and Examples of Pattern Recognition == | == Definition and Examples of Pattern Recognition == | ||
| − | + | Pattern Recognition is the art of assigning classes or categories to data. [[What is Pattern Recognition_OldKiwi|Continue reading...]]. | |
| − | + | == Decision Surfaces and Algebraic Geometry== | |
| − | + | Decision surfaces are the boundaries in the feature space that distinguish classes. [[Decision Surfaces_OldKiwi|Continue reading...]] | |
| − | == Decision Surfaces == | + | |
| − | + | ||
| − | + | ||
| − | Decision surfaces are the boundaries in the feature space that distinguish classes. | + | |
| − | + | ||
| − | + | ||
| − | + | ||
== Varieties == | == Varieties == | ||
| − | + | A Variety is a mathematical construct used to define a decision surface. [[Varieties_OldKiwi|Continue reading...]] | |
| − | + | ---- | |
| − | A Variety is a mathematical construct used to define a decision surface. | + | Next: [[Lecture_2_-_Decision_Hypersurfaces_OldKiwi|Lecture 2]] |
Latest revision as of 10:17, 10 June 2013
ECE662: Statistical Pattern Recognition and Decision Making Processes
Spring 2008, Prof. Boutin
Collectively created by the students in the class
Contents
Lecture 1 Lecture notes
Jump to: Outline| 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
This was the first day of class. These notes are from the class lecture.
Course Info
Textbook Information
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
Pattern Recognition is the art of assigning classes or categories to data. Continue reading....
Decision Surfaces and Algebraic Geometry
Decision surfaces are the boundaries in the feature space that distinguish classes. Continue reading...
Varieties
A Variety is a mathematical construct used to define a decision surface. Continue reading...
Next: Lecture 2
