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| + | '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' | ||
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| + | Spring 2008, [[user:mboutin|Prof. Boutin]] | ||
| − | = | + | <font size= 3> Slectures collectively created by the students in [[ECE662:BoutinSpring08_OldKiwi|the class]]</font size> |
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* [[Lecture 1 - Introduction_OldKiwi|Lecture 1 - Introduction]] | * [[Lecture 1 - Introduction_OldKiwi|Lecture 1 - Introduction]] | ||
* [[Lecture 2 - Decision Hypersurfaces_OldKiwi|Lecture 2 - Decision Hypersurfaces]] | * [[Lecture 2 - Decision Hypersurfaces_OldKiwi|Lecture 2 - Decision Hypersurfaces]] | ||
Latest revision as of 09:59, 4 November 2013
ECE662: Statistical Pattern Recognition and Decision Making Processes
Spring 2008, Prof. Boutin
Slectures collectively created by the students in the class
- Lecture 1 - Introduction
- Lecture 2 - Decision Hypersurfaces
- Lecture 3 - Bayes classification
- Lecture 4 - Bayes Classification
- Lecture 5 - Discriminant Functions
- Lecture 6 - Discriminant Functions
- Lecture 7 - MLE and BPE
- Lecture 8 - MLE, BPE and Linear Discriminant Functions
- Lecture 9 - Linear Discriminant Functions
- Lecture 10 - Batch Perceptron and Fisher Linear Discriminant
- Lecture 11 - Fischer's Linear Discriminant again
- Lecture 12 - Support Vector Machine and Quadratic Optimization Problem
- Lecture 13 - Kernel function for SVMs and ANNs introduction
- Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window)
- Lecture 15 - Parzen Window Method
- Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate
- Lecture 17 - Nearest Neighbors Clarification Rule and Metrics
- Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)
- Lecture 19 - Nearest Neighbor Error Rates
- Lecture 20 - Density Estimation using Series Expansion and Decision Trees
- Lecture 21 - Decision Trees(Continued)
- Lecture 22 - Decision Trees and Clustering
- Lecture 23 - Spanning Trees
- Lecture 24 - Clustering and Hierarchical Clustering
- Lecture 25 - Clustering Algorithms
- Lecture 26 - Statistical Clustering Methods
- Lecture 27 - Clustering by finding valleys of densities
- Lecture 28 - Final lecture
