(New page: [https://www.projectrhea.org/learning/slectures.php Slectures] *Slectures on Probability and Statistics **[[ECE662 Whitening and Coloring Transforms S14 MH|Whitening and Coloring Tra...)
 
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[https://www.projectrhea.org/learning/slectures.php Slectures]
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[[Category:slecture]]
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[[Category:ECE662Spring2014Boutin]]
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[[Category:ECE]]
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[[Category:ECE662]]
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[[Category:pattern recognition]]
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[[Category:statistics]]
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[[Category:probability]]
  
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<center><font size= 4>
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'''The [http://mireilleboutin.com Boutin] Lectures on Statistical Pattern Recognition'''
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</font size>
  
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*Slectures on Probability and Statistics
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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|Whitening and Coloring Transforms]], by [[User:Mhossain|Maliha Hossain]] <span style="color:GREEN">OK</span>  
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</center>
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**How to generate random n dimensional data from two categories with different priors  
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----
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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">OK</span>  
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==0. Foreword by Professor Boutin==
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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">OK</span>  
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==1. Background Material ==
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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">OK</span>  
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*Whitening and Coloring Transforms
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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">OK</span>
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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>  
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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">OK</span>  
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*How to generate random n dimensional data from two categories with different priors (use these methods to generate data for homework)
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**Principal Component Analysis (PCA)  
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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>  
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***[[PCA|Text slecture in English]], by [http://web.ics.purdue.edu/~zhou338/ Tian Zhou] <span style="color:GREEN">OK</span>  
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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>  
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***[[PCA Theory Examples|Text slecture in English]], by Sujin Jang <span style="color:GREEN">OK</span>  
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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>  
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***[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh <span style="color:GREEN">OK</span>  
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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>  
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***[[Pca khalid|Video slecture in English]], by Khalid Tahboub <span style="color:GREEN" />  
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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>
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*Slectures on Curse of Dimensionality  
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*Principal Component Analysis (PCA)  
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**[[Curse of Dimensionality|Text slecture in English]], and [[Curse of Dimensionality Chinese|in Chinese]] by Haonan Yu USE SAME COMMENT PAGE FOR BOTH ENGLISH AND CHINESE VERSION
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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>  
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*Slectures on Bayes Rule  
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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>
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**Bayes Rule in Layman's Terms  
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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>
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***[[Introduction to Bayes' Rule|Video slecture in Spanish]] by Francis Phillip <span style="color:GREEN">OK</span>
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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>
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**Derivation of Bayes Rule  
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*The Curse of Dimensionality  
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***[[Derivation of Bayes rule Anonymous7|Text slecture in English]] By Anonymous7 <span style="color:GREEN">OK</span>
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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>
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***[[From Bayes Theorem to Pattern Recognition via Bayes Rule|Text slecture in English]] by [http://varunvasudevan.com/ Varun Vasudevan] <span style="color:GREEN">OK</span>
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==2. Bayes Rule ==
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***[[Derivation of Bayes' Rule from Bayes' Theorem|Video slecture in English]] by Nadra Guizani <span style="color:GREEN">OK</span>
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*Bayes Rule in Layman's Terms  
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***[[Derivation Bayes Rule slecture ECE662 Spring2014 Kim|Video slecture in English]] by Jieun Kim <span style="color:GREEN">OK</span>
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**[[Introduction to Bayes' Rule|Video slecture in Spanish]] by Francis Phillip  
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***[[Derivation of Bayes rule In Greek|Text slecture in Greek]] by Stylianos Chatzidakis <span style="color:GREEN">OK</span>
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*Derivation of Bayes Rule  
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***[[Derivation of Bayes rule In Chinese|Text slecture in Chinese]] by Weibao Wang <span style="color:GREEN">OK</span>
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**[[Derivation of Bayes rule Anonymous7|Text slecture in English]] By Anonymous7  
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**Optimality of Bayes Rule  
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**[[From Bayes Theorem to Pattern Recognition via Bayes Rule|Text slecture in English]] by [http://varunvasudevan.com/ Varun Vasudevan]  
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***[[Slecture optimality bayes decision rule michaux ECE662S14|Video slecture in English]] by Aaron Michaux <span style="color:GREEN">OK</span>
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**[[Derivation of Bayes' Rule from Bayes' Theorem|Video slecture in English]] by Nadra Guizani  
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***[[Kjw810313|Video slecture in Korean]] by Jeong-wan Kim <span style="color:GREEN">OK</span>
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**[[Derivation Bayes Rule slecture ECE662 Spring2014 Kim|Video slecture in English]] by Jieun Kim  
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**Upper Bounds for Bayes Error  
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**[[Derivation of Bayes rule In Greek|Text slecture in Greek]] by Stylianos Chatzidakis
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***[[Upper Bounds for Bayes Error|Text slecture in English]] by G. M. Dilshan Godaliyadda   
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**[[Derivation of Bayes rule In Chinese|Text slecture in Chinese]] by Weibao Wang  
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***[[Upper Bound for Bayes error|Text slecture in English]] by Yihan Ding <span style="color:RED">MATERIAL PLAGIARIZED. NOT EVEN A CITATION IS GIVEN</span>
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*Optimality of Bayes Rule  
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***[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe <span style="color:GREEN">OK</span>
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**[[Slecture optimality bayes decision rule michaux ECE662S14|Video slecture in English]] by Aaron Michaux  
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**Bayes Rule to Minimize Risk  
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**[[Kjw810313|Video slecture in Korean]] by Jeong-wan Kim
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***[[Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014|Video slectures in English]], by Andy Park <span style="color:GREEN">OK</span>
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*Upper Bounds for Bayes Error  
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***[[Ness slecture 2014|Text slecture in Chinese]] by Robert Ness <span style="color:GREEN">OK</span>
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**[[Upper Bounds for Bayes Error|Text slecture in English]] by G. M. Dilshan Godaliyadda   
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***[[Bayes Rule Minimize Risk Dennis Lee|Text slecture in English]] by Dennis Lee <span style="color:GREEN">OK</span>
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**[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe
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**Bayes Rule for Normally Distributed Features  
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*Bayes Rule to Minimize Risk  
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***[[Discussion about Discriminant Functions for the Multivariate Normal Density|Text slecture in English]] by Yanzhe Cui  
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**[[Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014|Video slectures in English]], by Andy Park
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***[[Bayes Rule for 1-dimensional and N-dimensional feature spaces|Text slecture in English]] by Jihwan Lee <span style="color:GREEN">OK</span>
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**[[Ness slecture 2014|Text slecture in Chinese]] by Robert Ness  
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**Bayes rule in practice  
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**[[Bayes Rule Minimize Risk Dennis Lee|Text slecture in English]] by Dennis Lee
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***[[Bayes rule in practice|Text slecture in English]] by Lu Wang <span style="color:GREEN">OK</span>
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*Bayes Rule for Normally Distributed Features  
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***[[662slecture tang|Text slecture in English]] by Chuohao Tang <span style="color:GREEN">OK</span>
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**[[Discussion about Discriminant Functions for the Multivariate Normal Density|Text slecture in English]] by Yanzhe Cui  
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*Slectures on Neyman-Pearson test and ROC curves  
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**[[Bayes Rule for 1-dimensional and N-dimensional feature spaces|Text slecture in English]] by Jihwan Lee  
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**[[Neyman-Pearson Lemma and Receiver Operating Characteristic Curve|Text slecture in English]] by [https://engineering.purdue.edu/~lee714/ Soonam Lee] <span style="color:GREEN">OK</span>
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*Bayes rule in practice  
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**[[ROC curve analysis slecture ECE662 Spring0214 Sun|Video slecture in English]] by Jianxin Sun <span style="color:GREEN">OK</span>
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**[[Bayes rule in practice|Text slecture in English]] by Lu Wang
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**[[ECE662 roc|Text slecture in English]] by Hao Lin <span style="color:GREEN">OK</span>
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**[[662slecture tang|Text slecture in English]] by Chuohao Tang
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*Slectures on Density Estimation  
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*Neyman-Pearson test and ROC curves  
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**Maximum Likelihood Estimation (MLE)  
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**[[Neyman-Pearson Lemma and Receiver Operating Characteristic Curve|Text slecture in English]] by [https://engineering.purdue.edu/~lee714/ Soonam Lee]  
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***[[Mle tutorial|Text slecture in English]] by Sudhir Kylasa <span style="color:GREEN">OK</span>
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**[[ROC curve analysis slecture ECE662 Spring0214 Sun|Video slecture in English]] by Jianxin Sun  
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***[[Maximum Likelihood Estimation Analysis for various Probability Distributions|Text slecture in English]] by Hariharan Seshadri <span style="color:GREEN">OK</span>
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**[[ECE662 roc|Text slecture in English]] by Hao Lin
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***[[Video slecture in English: Introduction to Maximum Likelihood Estimation|Video slecture in English]] by Anantha Raghuraman <span style="color:GREEN">OK</span>
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==3. Global (parametric) Density Estimation Methods==
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***[[Convergence of the Maximum Likelihood Estimator over Multiple Trials|Video slecture in English]] by [http://web.ics.purdue.edu/~scarver/ Spencer Carver] <span style="color:GREEN">OK</span>
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*Maximum Likelihood Estimation (MLE)  
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***[[Maximum Likelihood Estimators and Examples|Text slecture in English]] by Lu Zhang <span style="color:GREEN">OK</span>
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**[[Mle tutorial|Text slecture in English]] by Sudhir Kylasa  
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***[[SlectureKeehwanECE662Spring14|Video slecture in English]] by Keehwan Park <span style="color:GREEN">OK</span>
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**[[Maximum Likelihood Estimation Analysis for various Probability Distributions|Text slecture in English]] by Hariharan Seshadri
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***[[MLEforGMM|Text slecture in English]] by Wen Yi  
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**[[Video slecture in English: Introduction to Maximum Likelihood Estimation|Video slecture in English]] by Anantha Raghuraman  
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***[[ECE662Selecture zhenpengMLE|Text slecture in English]] by Zhenpeng Zhao  
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**[[Convergence of the Maximum Likelihood Estimator over Multiple Trials|Video slecture in English]] by [http://web.ics.purdue.edu/~scarver/ Spencer Carver]  
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**Bayesian Estimation (BPE)  
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**[[Maximum Likelihood Estimators and Examples|Text slecture in English]] by Lu Zhang
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***[[Bayes Parameter Estimation|Text slecture in English]] by Haiguang Wen <span style="color:GREEN">OK</span>
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**[[SlectureKeehwanECE662Spring14|Video slecture in English]] by Keehwan Park  
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***[[Bayersian Parameter Estimation: Gaussian Case|Text slecture in English]], by Shaobo Fang  
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**[[MLEforGMM|Text slecture in English]] by Wen Yi  
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***[[Bayes Parameter Estimation with examples|Text slecture in English]] by Yu Wang   
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**[[ECE662Selecture zhenpengMLE|Text slecture in English]] by Zhenpeng Zhao  
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**Introduction to Local density Estimation Techniques (so-called "non-parametric")  
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*Bayesian Estimation (BPE)  
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***[[Introduction to local density estimation methods|Text slecture in English]] by Yu Liu <span style="color:GREEN">OK</span>
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**[[Bayes Parameter Estimation|Text slecture in English]] by Haiguang Wen
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***[[Slecture Introduction local density estimation methods ECE662 Spring2014 Aziza|Video slecture in Russian]] by Aziza Satkhozhina  
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**[[Bayersian Parameter Estimation: Gaussian Case|Text slecture in English]], by Shaobo Fang  
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***[[Introduction to local density estimation methods ECE662 Spring2014 Nusaybah|Video slecture in English]] by Nusaybah Abu-Mulaweh  
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**[[Bayes Parameter Estimation with examples|Text slecture in English]] by Yu Wang   
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***[[Intro local non parametric density estimation methods ECE662 Spring2014 Yuan|Video slecture in English]] by Chenxi Yuan <span style="color:GREEN">OK</span>
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==4. Local ("non-parametric") Density Estimation Methods==
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**Density Estimation with Parzen Windows  
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*Introduction to Local density Estimation Techniques (so-called "non-parametric")  
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***[[ParzenWindow|Text slecture in English]] by Chiho Choi <span style="color:GREEN">OK</span>
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**[[Introduction to local density estimation methods|Text slecture in English]] by Yu Liu
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***[[Parzen Window Density Estimation|Text slecture in English]] by Ben Foster <span style="color:GREEN">OK</span>
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**[[Slecture Introduction local density estimation methods ECE662 Spring2014 Aziza|Video slecture in Russian]] by Aziza Satkhozhina  
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***[[Parzen Windows|Text slecture in English]] by Abdullah Alshaibani <span style="color:GREEN">OK</span>
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**[[Introduction to local density estimation methods ECE662 Spring2014 Nusaybah|Video slecture in English]] by Nusaybah Abu-Mulaweh  
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**Density Estimation with K-Nearest Neighbors (KNN)  
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**[[Intro local non parametric density estimation methods ECE662 Spring2014 Yuan|Video slecture in English]] by Chenxi Yuan
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***[[KnnDensityEstimation|Text slecture in English]] by Raj Praveen Selvaraj <span style="color:GREEN">OK</span>
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*Density Estimation with Parzen Windows  
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***[[Knearestneighbors|Text slecture in English]] by Dan Barrett QUESTION PAGE
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**[[ParzenWindow|Text slecture in English]] by Chiho Choi
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***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span style="color:GREEN">OK</span>
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**[[Parzen Window Density Estimation|Text slecture in English]] by Ben Foster  
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**The Nearest Neighbor Decision Rule  
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**[[Parzen Windows|Text slecture in English]] by Abdullah Alshaibani  
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***[[Estimation Using Nearest Neighbor|Text slecture in English]] by Sang Ho Yoon
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*Density Estimation with K-Nearest Neighbors (KNN)  
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***[[Slecture from KNN to nearest neighbor|Text slecture in English]] by Jonathan Manring <span style="color:GREEN">OK</span>
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**[[KnnDensityEstimation|Text slecture in English]] by Raj Praveen Selvaraj  
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*Slectures on Linear Classifiers
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**[[Knearestneighbors|Text slecture in English]] by Dan Barrett
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**[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang
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*The Nearest Neighbor Decision Rule  
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**[[Estimation Using Nearest Neighbor|Text slecture in English]] by Sang Ho Yoon
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**[[Slecture from KNN to nearest neighbor|Text slecture in English]] by Jonathan Manring  
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==5. Linear Classifiers ==
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*Linear classifiers, projective coordinates, and Fisher linear discriminant
 
**[[JMSLinearClassifierSlecture|Text slecture in English]] by John Mulcahy-Stanislawczyk  
 
**[[JMSLinearClassifierSlecture|Text slecture in English]] by John Mulcahy-Stanislawczyk  
 
**[[CBR_logistic_regression|Text slecture in English]] by Borui Chen
 
**[[CBR_logistic_regression|Text slecture in English]] by Borui Chen
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*Slectures on Support Vector Machines (SVM)  
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*Support Vector Machines (SVM)  
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**[https://kiwi.ecn.purdue.edu/rhea/index.php/Least_Squares_Support_Vector_Machine_and_its_Applications_in_Solving_Linear_Regression_Problems Text slecture in English] by Xing Liu  
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**[[Least_Squares_Support_Vector_Machine_and_its_Applications_in_Solving_Linear_Regression_Problems| Text slecture in English]] by Xing Liu  
 
**[[Support Vector Machine|Video slecture in English]] by Tao Jiang  
 
**[[Support Vector Machine|Video slecture in English]] by Tao Jiang  
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*Slectures on Clustering Algorithms (supplemental material)
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==6. Supplementary Material==
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*Clustering Algorithms  
 
**[[SlectureDavidRunyanCS662Spring14|text slecture in English]] by David Runyan
 
**[[SlectureDavidRunyanCS662Spring14|text slecture in English]] by David Runyan
 
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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

Retrieved from "https://www.projectrhea.org/rhea/index.php?title=2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures&oldid=68939"
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Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett