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<div style="border-bottom: rgb(68,68,136) 1px solid; border-left: rgb(51,51,136) 4px solid; padding-bottom: 2em; margin: auto; padding-left: 2em; width: 30em; padding-right: 2em; background: rgb(238,238,255); border-top: rgb(68,68,136) 1px solid; border-right: rgb(68,68,136) 1px solid; padding-top: 2em">
 
<div style="border-bottom: rgb(68,68,136) 1px solid; border-left: rgb(51,51,136) 4px solid; padding-bottom: 2em; margin: auto; padding-left: 2em; width: 30em; padding-right: 2em; background: rgb(238,238,255); border-top: rgb(68,68,136) 1px solid; border-right: rgb(68,68,136) 1px solid; padding-top: 2em">
 
== &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; '''Welcome to ECE662!'''  ==
 
== &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; '''Welcome to ECE662!'''  ==
−
* The cleaned up version of the slectures is [[2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures|here]].  
+
 
 +
*The cleaned up version of the slectures is [[2014 Spring ECE 662 Boutin Statistical Pattern recognition slectures|here]].  
 
*Please fill this [https://docs.google.com/forms/d/1aYxQF5iEbJrd2OBVVxgABMqnlkOtN--r0lxCGpsX-Vw/viewform form] (by May 18) to help us keep track of the diversity in the class. It is voluntary, anonymous and would not take more than 5 minutes. Thank You.
 
*Please fill this [https://docs.google.com/forms/d/1aYxQF5iEbJrd2OBVVxgABMqnlkOtN--r0lxCGpsX-Vw/viewform form] (by May 18) to help us keep track of the diversity in the class. It is voluntary, anonymous and would not take more than 5 minutes. Thank You.
 
</div>  
 
</div>  
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== [https://www.projectrhea.org/learning/slectures.php Slectures]  ==
 
== [https://www.projectrhea.org/learning/slectures.php Slectures]  ==
−
The cleaned up version of these slectures is [[2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures|HERE]]
+
 
 +
The cleaned up version of these slectures is [[2014 Spring ECE 662 Boutin Statistical Pattern recognition slectures|HERE]]  
  
 
Please use this [[Slecture template ECE662S14|template for text slectures]] or this [[Slecture template video ECE662S14|template for video slectures]]  
 
Please use this [[Slecture template ECE662S14|template for text slectures]] or this [[Slecture template video ECE662S14|template for video slectures]]  
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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>  
 
***[[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>  
 
***[[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>  
 
***[[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>  
−
***[[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>
+
***[[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>  
 
***[[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>  
 
***[[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>  
 
**Principal Component Analysis (PCA)  
 
**Principal Component Analysis (PCA)  
Line 47: Line 49:
 
***[[PCA Theory Examples|Text slecture in English]], by Sujin Jang <span style="color:GREEN">OK</span>  
 
***[[PCA Theory Examples|Text slecture in English]], by Sujin Jang <span style="color:GREEN">OK</span>  
 
***[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh <span style="color:GREEN">OK</span>  
 
***[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh <span style="color:GREEN">OK</span>  
−
***[[Pca khalid|Video slecture in English]], by Khalid Tahboub <span style="color:GREEN" />
+
***[[Pca khalid|Video slecture in English]], by Khalid Tahboub &lt;span style="color:GREEN" /&gt;
 
*Slectures on Curse of Dimensionality  
 
*Slectures on Curse of Dimensionality  
 
**[[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  
 
**[[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  
Line 64: Line 66:
 
***[[Kjw810313|Video slecture in Korean]] by Jeong-wan Kim <span style="color:GREEN">OK</span>  
 
***[[Kjw810313|Video slecture in Korean]] by Jeong-wan Kim <span style="color:GREEN">OK</span>  
 
**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 <span style="color:RED">MATERIAL PLAGIARIZED. NOT EVEN A CITATION IS GIVEN</span>  
 
***[[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>  
 
***[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe <span style="color:GREEN">OK</span>  
 
***[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe <span style="color:GREEN">OK</span>  
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*Slectures on Neyman-Pearson test and ROC curves  
 
*Slectures on Neyman-Pearson test and ROC curves  
 
**[[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>  
 
**[[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>  
−
**[[ROC curve analysis slecture ECE662 Spring0214 Sun|Video slecture in English]] by Jianxin Sun <span style="color:GREEN">OK</span>  
+
**[[ROC curve analysis slecture ECE662 Spring0214 Sun|Video slecture in English]] by Jianxin Sun <span style="color:GREEN">OK</span><br>  
−
**[[ECE662 roc|Text slecture in English]] by Hao Lin <span style="color:GREEN">OK</span>  
+
 
*Slectures on Density Estimation  
 
*Slectures on Density Estimation  
 
**Maximum Likelihood Estimation (MLE)  
 
**Maximum Likelihood Estimation (MLE)  
Line 94: Line 95:
 
***[[Bayes Parameter Estimation|Text slecture in English]] by Haiguang Wen <span style="color:GREEN">OK</span>  
 
***[[Bayes Parameter Estimation|Text slecture in English]] by Haiguang Wen <span style="color:GREEN">OK</span>  
 
***[[Bayersian Parameter Estimation: Gaussian Case|Text slecture in English]], by Shaobo Fang  
 
***[[Bayersian Parameter Estimation: Gaussian Case|Text slecture in English]], by Shaobo Fang  
−
***[[Bayes Parameter Estimation with examples|Text slecture in English]] by Yu Wang
+
***[[Bayes Parameter Estimation with examples|Text slecture in English]] by Yu Wang  
 
**Introduction to Local density Estimation Techniques (so-called "non-parametric")  
 
**Introduction to Local density Estimation Techniques (so-called "non-parametric")  
 
***[[Introduction to local density estimation methods|Text slecture in English]] by Yu Liu <span style="color:GREEN">OK</span>  
 
***[[Introduction to local density estimation methods|Text slecture in English]] by Yu Liu <span style="color:GREEN">OK</span>  
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***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span style="color:GREEN">OK</span>  
 
***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span style="color:GREEN">OK</span>  
 
**The Nearest Neighbor Decision Rule  
 
**The Nearest Neighbor Decision Rule  
−
***[[Estimation Using Nearest Neighbor|Text slecture in English]] by Sang Ho Yoon
+
***[[Estimation Using Nearest Neighbor|Text slecture in English]] by Sang Ho Yoon  
 
***[[Slecture from KNN to nearest neighbor|Text slecture in English]] by Jonathan Manring <span style="color:GREEN">OK</span>  
 
***[[Slecture from KNN to nearest neighbor|Text slecture in English]] by Jonathan Manring <span style="color:GREEN">OK</span>  
 
*Slectures on Linear Classifiers  
 
*Slectures on Linear Classifiers  
 
**[[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  
 
*Slectures on Support Vector Machines (SVM)  
 
*Slectures on Support Vector Machines (SVM)  
 
**[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  
 
**[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  

Revision as of 17:22, 15 May 2014



ECE662: Statistical Pattern Recognition and Decision Making Processes, Spring 2014 (cross-listed with CS662)

                Welcome to ECE662!

  • The cleaned up version of the slectures is here.
  • Please fill this form (by May 18) to help us keep track of the diversity in the class. It is voluntary, anonymous and would not take more than 5 minutes. Thank You.

Course Information

Instructor:

Office: MSEE342
Office hours
Assignment Drop Box

Lecture:

  • When? TuTh, 10:30 - 11:45
  • Where? EE117 (subject to change)

Slectures

The cleaned up version of these slectures is HERE

Please use this template for text slectures or this template for video slectures

Peer Reviews

Discussion

Feel free to use the space below for discussion, or create a page for discussion and link it below.

Back to main ECE662 page

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Alumni Liaison

Meet a recent graduate heading to Sweden for a Postdoctorate.

Christine Berkesch