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Multilingual [https://www.projectrhea.org/learning/slectures.php Slectures] by Students in the Spring 2014 Class of [[ECE662]]
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Multilingual [http://www.projectrhea.org/learning/slectures.php Slectures] by Students in the Spring 2014 Class of [[ECE662]]
 
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**[[ECE662 Whitening and Coloring Transforms S14 MH|Text slecture in English]], by [[User:Mhossain|Maliha Hossain]] <span style="color:GREEN">Very clear!</span>  
 
**[[ECE662 Whitening and Coloring Transforms S14 MH|Text slecture in English]], by [[User:Mhossain|Maliha Hossain]] <span style="color:GREEN">Very clear!</span>  
 
*How to generate random n dimensional data from two categories with different priors (use these methods to generate data for homework)  
 
*How to generate random n dimensional data from two categories with different priors (use these methods to generate data for homework)  
**[[Generating random data with controlled prior probabilities slecture ECE662S14 Gheith|Video slecture in English]] by Alex Gheith   
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**[[Generating random data with controlled prior probabilities slecture ECE662S14 Gheith|Video slecture in English]] by Alex Gheith  <span style="color:GREEN">Newbies can start here</span>
**[[How to generate random n dimensional data from two categories with different priors slecture Minwoong Kim ECE662 Spring 2014|Video slecture in Korean ]], by Minwoong Kim  
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**[[How to generate random n dimensional data from two categories with different priors slecture Minwoong Kim ECE662 Spring 2014|Video slecture in Korean ]], by Minwoong Kim <span style="color:GREEN">Newbies can start here- if they speak Korean</span>
**[[How to generate random n dimensional data from two categories with different priors slecture Minwoong Cho ECE662 Spring 2014|Video slecture in Korean ]], by Hyun Dok Cho  
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**[[How to generate random n dimensional data from two categories with different priors slecture Minwoong Cho ECE662 Spring 2014|Video slecture in Korean ]], by Hyun Dok Cho <span style="color:GREEN">Newbies can start here- if you they speak Korean</span>
**[[The principles for how to generate random samples from a Gaussian distribution|Text slecture in English]] by Joonsoo Kim  
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**[[The principles for how to generate random samples from a Gaussian distribution|Text slecture in English]] by Joonsoo Kim <span style="color:GREEN">More Advanced</span>
**[[Generation of N-dimensional normally distributed random numbers from two categories with different priors|Text slecture in English]] by Jonghoon Jin  
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**[[Generation of N-dimensional normally distributed random numbers from two categories with different priors|Text slecture in English]] by Jonghoon Jin <span style="color:GREEN">More Advanced</span> 
 
*Principal Component Analysis (PCA)  
 
*Principal Component Analysis (PCA)  
**[[PCA|Text slecture in English]], by [http://web.ics.purdue.edu/~zhou338/ Tian Zhou]   
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**[[PCA|Text slecture in English]], by [http://web.ics.purdue.edu/~zhou338/ Tian Zhou]  <span style="color:GREEN">Starts very slowly.</span>
**[[PCA Theory Examples|Text slecture in English]], by Sujin Jang   
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**[[PCA Theory Examples|Text slecture in English]], by Sujin Jang  <span style="color:GREEN">Jumps right into linear algebra at the beginning.</span>
**[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh
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**[[Pca khalid|Video slecture in English]], by Khalid Tahboub <span style="color:GREEN">Clearly explains why it's not good when trying to recognize patterns</span>
**[[Pca khalid|Video slecture in English]], by Khalid Tahboub
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**[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh <span style="color:GREEN">More Advanced. Covers kernel PCA.</span>
 
*The Curse of Dimensionality  
 
*The Curse of Dimensionality  
**[[Curse of Dimensionality|Text slecture in English]], and [[Curse of Dimensionality Chinese|in Chinese]] by Haonan Yu  
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**[[Curse of Dimensionality|Text slecture in English]], and [[Curse of Dimensionality Chinese|in Chinese]] by Haonan Yu <span style="color:GREEN">Text flows nicely. Fun read.</span>
 
==2. Bayes Rule ==
 
==2. Bayes Rule ==
 
*Bayes Rule in Layman's Terms  
 
*Bayes Rule in Layman's Terms  
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*Upper Bounds for Bayes Error  
 
*Upper Bounds for Bayes Error  
 
**[[Upper Bounds for Bayes Error|Text slecture in English]] by G. M. Dilshan Godaliyadda   
 
**[[Upper Bounds for Bayes Error|Text slecture in English]] by G. M. Dilshan Godaliyadda   
**[[Upper Bound for Bayes error|Text slecture in English]] by Yihan Ding
 
 
**[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe   
 
**[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe   
 
*Bayes Rule to Minimize Risk  
 
*Bayes Rule to Minimize Risk  
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==3. Global (parametric) Density Estimation Methods==  
 
==3. Global (parametric) Density Estimation Methods==  
 
*Maximum Likelihood Estimation (MLE)  
 
*Maximum Likelihood Estimation (MLE)  
**[[Mle tutorial|Text slecture in English]] by Sudhir Kylasa >
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**[[Mle tutorial|Text slecture in English]] by Sudhir Kylasa  
 
**[[Maximum Likelihood Estimation Analysis for various Probability Distributions|Text slecture in English]] by Hariharan Seshadri   
 
**[[Maximum Likelihood Estimation Analysis for various Probability Distributions|Text slecture in English]] by Hariharan Seshadri   
 
**[[Video slecture in English: Introduction to Maximum Likelihood Estimation|Video slecture in English]] by Anantha Raghuraman  
 
**[[Video slecture in English: Introduction to Maximum Likelihood Estimation|Video slecture in English]] by Anantha Raghuraman  

Latest revision as of 10:38, 22 January 2015


The Boutin Lectures on Statistical Pattern Recognition

Multilingual Slectures by Students in the Spring 2014 Class of ECE662


0. Foreword by Professor Boutin

1. Background Material

2. Bayes Rule

3. Global (parametric) Density Estimation Methods

4. Local ("non-parametric") Density Estimation Methods

5. Linear Classifiers

6. Supplementary Material


Go to ECE662 Spring 2014 Course Wiki

Go to Slecture Page

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva