(New page: = SA 10503: Introduction to Machine Learning & Pattern Recognition= Maymester Course in Turkey 250px ==Peer Legacy== Read what students who previously took this cou...) |
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= SA 10503: Introduction to Machine Learning & Pattern Recognition= | = SA 10503: Introduction to Machine Learning & Pattern Recognition= | ||
Maymester Course in Turkey | Maymester Course in Turkey | ||
| − | + | *[[Maymester_Intro_to_Machine_learning_2011|Official announcement for 2011]] | |
| − | [[ | + | *[[Media:2010_Turkey.pdf|Powerpoint presentation for 2010]] |
| − | + | ---- | |
==Peer Legacy== | ==Peer Legacy== | ||
Read what students who previously took this course have to say about it on the [[Peer_Legacy_SA10503|Peer Legacy page]] of this course. | Read what students who previously took this course have to say about it on the [[Peer_Legacy_SA10503|Peer Legacy page]] of this course. | ||
| − | == | + | ---- |
| − | + | == Related Topics == | |
| − | + | * [[About Pattern Recognition]] | |
| − | + | * [[Bayes_Decision_Theory]] | |
| − | + | * [[Discriminant Functions]] | |
| − | + | * [[Fisher Linear Discriminant]] | |
| − | + | * [[Bayesian Decision Theory for Normally Distributed Features]] | |
| − | + | * [[Feature Extraction]] | |
| − | + | * [[Density Estimation]] | |
| − | + | * [[Linear classifiers]] | |
| − | + | * [[Artificial Neural Networks]] | |
| − | + | * [[Support Vector Machines]] | |
| − | + | * [[Clustering]] | |
| − | + | * [[Decision Trees]] | |
| − | == | + | ---- |
| − | * | + | == Interesting Rhea pages related to machine learning == |
| − | + | *[[ECE662:Glossary_Old_Kiwi|Decision Theory Glossary]] | |
| − | + | *[[Parametric_Estimators_OldKiwi|About Parametric Estimators]] | |
| + | *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] | ||
| + | *[[Fisher_discriminant_under_nonlinear_data|Fisher linear discriminant can be used for non-linearly separable data too!]] | ||
| + | *[[ANN_Simulink_examples_ece662_Sp2010|A jump start on using Simulink to develop a ANN-based classifier]] | ||
---- | ---- | ||
[[Meta_Course_List|Back to Course list]] | [[Meta_Course_List|Back to Course list]] | ||
Latest revision as of 05:47, 18 November 2010
Contents
SA 10503: Introduction to Machine Learning & Pattern Recognition
Maymester Course in Turkey
Peer Legacy
Read what students who previously took this course have to say about it on the Peer Legacy page of this course.
Related Topics
- About Pattern Recognition
- Bayes_Decision_Theory
- Discriminant Functions
- Fisher Linear Discriminant
- Bayesian Decision Theory for Normally Distributed Features
- Feature Extraction
- Density Estimation
- Linear classifiers
- Artificial Neural Networks
- Support Vector Machines
- Clustering
- Decision Trees
- Decision Theory Glossary
- About Parametric Estimators
- Bayes rule under severe class imbalance
- Fisher linear discriminant can be used for non-linearly separable data too!
- A jump start on using Simulink to develop a ANN-based classifier
