m
 
(275 intermediate revisions by 48 users not shown)
Line 1: Line 1:
−
<p><br />  
+
<br>  
−
</p><p><br />  
+
 
−
</p>
+
<br>  
−
<h2> <a _fcknotitle="true" href="ECE662">ECE662</a>: <b>Statistical Pattern Recognition and Decision Making Processes, Spring 2014</b> (cross-listed with CS662)  </h2>
+
 
−
<hr />
+
== [[ECE662]]: '''Statistical Pattern Recognition and Decision Making Processes, Spring 2014''' (cross-listed with CS662)  ==
 +
 
 +
----
 
<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">
−
<h2> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <b>Welcome to ECE662!</b> </h2>
+
== &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; '''Welcome to ECE662!''' ==
−
<ul><li>When you see the peer review of your work in your dropbox, do not click "mark as read". If you do, then the review will disappear.
+
* The cleaned up version of the slectures is [[2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures|here]].  
−
</li></ul>
+
 
</div>  
 
</div>  
−
<hr />
+
----
−
<h2> <b>Course Information</b> </h2>
+
 
−
<p>Instructor:  
+
== '''Course Information''' ==
−
</p>
+
 
−
<ul><li><a href="User:Mboutin">Professor Mimi Boutin</a>
+
Instructor:  
−
</li></ul>
+
 
−
<dl><dd><dl><dd>Office: MSEE342  
+
*[[User:Mboutin|Professor Mimi Boutin]]
−
</dd><dd><a href="Open office hours mboutin">Office hours</a>
+
 
−
</dd><dd><a href="https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=mboutin&amp;assn=true">Assignment Drop Box</a>
+
::Office: MSEE342  
−
</dd></dl>
+
::[[Open office hours mboutin|Office hours]]
−
</dd></dl>
+
::[https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=mboutin&assn=true Assignment Drop Box]
−
<p>Lecture:  
+
 
−
</p>
+
Lecture:  
−
<ul><li><b>When?</b> TuTh, 10:30 - 11:45  
+
 
−
</li><li><b>Where?</b> EE117 (subject to change)
+
*'''When?''' TuTh, 10:30 - 11:45  
−
</li></ul>
+
*'''Where?''' EE117 (subject to change)
−
<hr />
+
 
−
<h2> <a href="https://www.projectrhea.org/learning/slectures.php">Slectures</a> </h2>
+
----
−
<p>Please use this <a href="Slecture template ECE662S14">template for text slectures</a> or this <a href="Slecture template video ECE662S14">template for video slectures</a>
+
 
−
</p>
+
== [https://www.projectrhea.org/learning/slectures.php Slectures] ==
−
<ul><li>Slectures on Probability and Statistics  
+
The cleaned up version of these slectures is [[2014_Spring_ECE_662_Boutin_Statistical_Pattern_recognition_slectures|HERE]]
−
<ul><li><a href="ECE662 Whitening and Coloring Transforms S14 MH">Whitening and Coloring Transforms</a>, by <a href="User:Mhossain">Maliha Hossain</a>  
+
 
−
</li><li><a href="How to generate random n dimensional data from two categories with different priors slecture Minwoong Kim ECE662 Spring 2014">How to generate n-D Gaussian data in the two category case </a>, by Minwoong Kim (in Korean)
+
Please use this [[Slecture template ECE662S14|template for text slectures]] or this [[Slecture template video ECE662S14|template for video slectures]]
−
</li><li><a href="PCA">Principal Component Analysis (PCA)</a>, by <a href="http://web.ics.purdue.edu/~zhou338/">Tian Zhou</a>  
+
 
−
</li></ul>
+
*Slectures on Probability and Statistics  
−
</li><li>Slectures on Bayes Rule  
+
**[[ECE662 Whitening and Coloring Transforms S14 MH|Whitening and Coloring Transforms]], by [[User:Mhossain|Maliha Hossain]] <span style="color:GREEN">OK</span>  
−
<ul><li><a href="From Bayes Theorem to Pattern Recognition via Bayes Rule">From Bayes' Theorem to Pattern Recognition via Bayes' Rule</a> by <a href="http://varunvasudevan.com/">Varun Vasudevan</a>  
+
**How to generate random n dimensional data from two categories with different priors
−
</li><li><a href="Upper Bounds for Bayes Error">Upper Bounds for Bayes Error</a> by G. M. Dilshan Godaliyadda  
+
***[[Generating random data with controlled prior probabilities slecture ECE662S14 Gheith|Video slecture in English]] by Alex Gheith <span style="color:GREEN">OK</span>  
−
</li><li><a href="Test">Upper Bounds for Bayes Error (including the derivation of Chernoff Distance)</a> by Jeehyun Choe  
+
***[[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>
−
</li><li><a href="Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014">Bayes Rule to minimize risk</a>, by Andy Park  
+
***[[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>  
−
</li><li><a href="Bayes Rule Minimize Risk Dennis Lee">Bayes Rule for Minimizing Risk</a> by Dennis Lee
+
***[[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>
−
</li><li>Link to slecture (use a descriptive title)
+
***[[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>
−
</li></ul>
+
**Principal Component Analysis (PCA)  
−
</li><li>Slectures on Density Estimation  
+
***[[PCA|Text slecture in English]], by [http://web.ics.purdue.edu/~zhou338/ Tian Zhou] <span style="color:GREEN">OK</span>  
−
<ul><li><a href="Mle tutorial">Tutorial on Maximum Likelihood Estimates</a> by Sudhir Kylasa
+
***[[PCA Theory Examples|Text slecture in English]], by Sujin Jang <span style="color:GREEN">OK</span>  
−
</li><li><a href="Introduction to local density estimation methods">Introduction to local (nonparametric) density estimation methods</a> by <a href="https://www.youtube.com/watch?v=WwPpsLjUsfQ">Yu Liu</a>  
+
***[[Kernel PCA|Video slecture in English, and Chinese]], by Tsung Tai Yeh <span style="color:GREEN">OK</span>  
−
</li></ul>
+
***[[Pca khalid|Video slecture in English]], by Khalid Tahboub <span style="color:GREEN" />  
−
</li><li>Slectures on Linear Classifiers
+
*Slectures on Curse of Dimensionality
−
</li></ul>
+
**[[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
−
<hr />
+
*Slectures on Bayes Rule  
−
<h2> Peer Reviews  </h2>
+
**Bayes Rule in Layman's Terms
−
<ul><li><a href="Instructions peer review hw1">Instruction for peer reviewing HW1</a>
+
***[[Introduction to Bayes' Rule|Video slecture in Spanish]] by Francis Phillip <span style="color:GREEN">OK</span>  
−
</li><li>Heads up: peer review of hw2 will be due April 29
+
**Derivation of Bayes Rule
−
</li></ul>
+
***[[Derivation of Bayes rule Anonymous7|Text slecture in English]] By Anonymous7 <span style="color:GREEN">OK</span>
−
<hr />
+
***[[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>
−
<h2> Discussion  </h2>
+
***[[Derivation of Bayes' Rule from Bayes' Theorem|Video slecture in English]] by Nadra Guizani <span style="color:GREEN">OK</span>  
−
<p>Feel free to use the space below for discussion, or create a page for discussion and link it below.  
+
***[[Derivation Bayes Rule slecture ECE662 Spring2014 Kim|Video slecture in English]] by Jieun Kim <span style="color:GREEN">OK</span>
−
</p>
+
***[[Derivation of Bayes rule In Greek|Text slecture in Greek]] by Stylianos Chatzidakis <span style="color:GREEN">OK</span>  
−
<ul><li><a href="Data discussion HW1 ECE662 S14 Boutin">Where to find data for HW1</a>
+
***[[Derivation of Bayes rule In Chinese|Text slecture in Chinese]] by Weibao Wang <span style="color:GREEN">OK</span>  
−
</li><li><a href="Yelp Dataset">Possible Real-world data to use for class</a>
+
**Optimality of Bayes Rule
−
</li><li><a href="Programming help ECE662S14">Programming help!</a>
+
***[[Slecture optimality bayes decision rule michaux ECE662S14|Video slecture in English]] by Aaron Michaux <span style="color:GREEN">OK</span>
−
</li><li>New Discussion
+
***[[Kjw810313|Video slecture in Korean]] by Jeong-wan Kim <span style="color:GREEN">OK</span>
−
</li></ul>
+
**Upper Bounds for Bayes Error  
−
<hr />
+
***[[Upper Bounds for Bayes Error|Text slecture in English]] by G. M. Dilshan Godaliyadda
−
<p><a href="ECE662">Back to main ECE662 page</a>
+
***[[Test|Text slecture in English (includes the derivation of Chernoff Distance)]] by Jeehyun Choe <span style="color:GREEN">OK</span>  
−
</p><a _fcknotitle="true" href="Category:ECE662Spring2014Boutin">ECE662Spring2014Boutin</a> <a _fcknotitle="true" href="Category:ECE">ECE</a> <a _fcknotitle="true" href="Category:ECE662">ECE662</a> <a _fcknotitle="true" href="Category:Pattern_recognition">Pattern_recognition</a>
+
**Bayes Rule to Minimize Risk
 +
***[[Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014|Video slectures in English]], by Andy Park <span style="color:GREEN">OK</span>  
 +
***[[Ness slecture 2014|Text slecture in Chinese]] by Robert Ness <span style="color:GREEN">OK</span>
 +
***[[Bayes Rule Minimize Risk Dennis Lee|Text slecture in English]] by Dennis Lee <span style="color:GREEN">OK</span>
 +
**Bayes Rule for Normally Distributed Features
 +
***[[Discussion about Discriminant Functions for the Multivariate Normal Density|Text slecture in English]] by Yanzhe Cui
 +
***[[Bayes Rule for 1-dimensional and N-dimensional feature spaces|Text slecture in English]] by Jihwan Lee <span style="color:GREEN">OK</span>  
 +
**Bayes rule in practice
 +
***[[Bayes rule in practice|Text slecture in English]] by Lu Wang <span style="color:GREEN">OK</span>  
 +
***[[662slecture tang|Text slecture in English]] by Chuohao Tang <span style="color:GREEN">OK</span>  
 +
*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>  
 +
**[[ROC curve analysis slecture ECE662 Spring0214 Sun|Video slecture in English]] by Jianxin Sun <span style="color:GREEN">OK</span>
 +
*Slectures on Density Estimation  
 +
**Maximum Likelihood Estimation (MLE)
 +
***[[Mle tutorial|Text slecture in English]] by Sudhir Kylasa <span style="color:GREEN">OK</span>  
 +
***[[Maximum Likelihood Estimation Analysis for various Probability Distributions|Text slecture in English]] by Hariharan Seshadri <span style="color:GREEN">OK</span>
 +
***[[Video slecture in English: Introduction to Maximum Likelihood Estimation|Video slecture in English]] by Anantha Raghuraman <span style="color:GREEN">OK</span>  
 +
***[[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>
 +
***[[Maximum Likelihood Estimators and Examples|Text slecture in English]] by Lu Zhang <span style="color:GREEN">OK</span>  
 +
***[[SlectureKeehwanECE662Spring14|Video slecture in English]] by Keehwan Park <span style="color:GREEN">OK</span>
 +
***[[MLEforGMM|Text slecture in English]] by Wen Yi
 +
***[[ECE662Selecture zhenpengMLE|Text slecture in English]] by Zhenpeng Zhao
 +
**Bayesian Estimation (BPE)
 +
***[[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
 +
***[[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 methods|Text slecture in English]] by Yu Liu <span style="color:GREEN">OK</span>
 +
***[[Slecture Introduction local density estimation methods ECE662 Spring2014 Aziza|Video slecture in Russian]] by Aziza Satkhozhina
 +
***[[Introduction to local density estimation methods ECE662 Spring2014 Nusaybah|Video slecture in English]] by Nusaybah Abu-Mulaweh
 +
***[[Intro local non parametric density estimation methods ECE662 Spring2014 Yuan|Video slecture in English]] by Chenxi Yuan <span style="color:GREEN">OK</span>  
 +
**Density Estimation with Parzen Windows
 +
***[[ParzenWindow|Text slecture in English]] by Chiho Choi <span style="color:GREEN">OK</span>  
 +
***[[Parzen Window Density Estimation|Text slecture in English]] by Ben Foster <span style="color:GREEN">OK</span>  
 +
***[[Parzen Windows|Text slecture in English]] by Abdullah Alshaibani <span style="color:GREEN">OK</span>  
 +
**Density Estimation with K-Nearest Neighbors (KNN)
 +
***[[KnnDensityEstimation|Text slecture in English]] by Raj Praveen Selvaraj <span style="color:GREEN">OK</span>  
 +
***[[Knearestneighbors|Text slecture in English]] by Dan Barrett QUESTION PAGE
 +
***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span style="color:GREEN">OK</span>  
 +
**The Nearest Neighbor Decision Rule
 +
***[[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>  
 +
*Slectures on Linear Classifiers
 +
**[[JMSLinearClassifierSlecture|Text slecture in English]] by John Mulcahy-Stanislawczyk
 +
**[[CBR_logistic_regression|Text slecture in English]] by Borui Chen
 +
*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
 +
**[[Support Vector Machine|Video slecture in English]] by Tao Jiang
 +
*Slectures on Clustering Algorithms (supplemental material)
 +
**[[SlectureDavidRunyanCS662Spring14|text slecture in English]] by David Runyan
 +
 
 +
----
 +
 
 +
== Peer Reviews  ==
 +
 
 +
*[[Instructions peer review hw1|Instruction for peer reviewing HW1]]
 +
*[[Instructions peer review hw2|Instruction for peer reviewing HW2]] (due before class on April 29)
 +
 
 +
----
 +
 
 +
== Discussion  ==
 +
 
 +
Feel free to use the space below for discussion, or create a page for discussion and link it below.  
 +
 
 +
*[[Data discussion HW1 ECE662 S14 Boutin|Where to find data for HW1]]
 +
*[[Yelp Dataset|Possible Real-world data to use for class]]
 +
*[[Programming help ECE662S14|Programming help!]]
 +
*New Discussion
 +
 
 +
----
 +
 
 +
[[ECE662|Back to main ECE662 page]]
 +
 
 +
[[Category:ECE662Spring2014Boutin]] [[Category:ECE]] [[Category:ECE662]] [[Category:Pattern_recognition]]

Latest revision as of 19:50, 2 May 2016



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.

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

Retrieved from "https://www.projectrhea.org/rhea/index.php?title=2014_Spring_ECE_662_Boutin&oldid=71613"
Shortcuts
Help Main Wiki Page Random Page Special Pages Log in
Search

Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood