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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) | *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 <span style="color:GREEN">Newbies start here</span> | **[[Generating random data with controlled prior probabilities slecture ECE662S14 Gheith|Video slecture in English]] by Alex Gheith <span style="color:GREEN">Newbies 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 | + | **[[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 start here- if you 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 | **[[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 | ||
**[[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> | **[[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> | ||
Revision as of 10:15, 22 May 2014
The Boutin Lectures on Statistical Pattern Recognition
Multilingual Slectures by Students in the Spring 2014 Class of ECE662
Contents
0. Foreword by Professor Boutin
1. Background Material
- Whitening and Coloring Transforms
- Text slecture in English, by Maliha Hossain Very clear!
- How to generate random n dimensional data from two categories with different priors (use these methods to generate data for homework)
- Video slecture in English by Alex Gheith Newbies start here
- Video slecture in Korean , by Minwoong Kim Newbies start here- if you speak Korean
- Video slecture in Korean , by Hyun Dok Cho
- Text slecture in English by Joonsoo Kim More Advanced
- Text slecture in English by Jonghoon Jin More Advanced
- Principal Component Analysis (PCA)
- Text slecture in English, by Tian Zhou
- Text slecture in English, by Sujin Jang
- Video slecture in English, and Chinese, by Tsung Tai Yeh
- Video slecture in English, by Khalid Tahboub
- The Curse of Dimensionality
- Text slecture in English, and in Chinese by Haonan Yu
2. Bayes Rule
- Bayes Rule in Layman's Terms
- Video slecture in Spanish by Francis Phillip
- Derivation of Bayes Rule
- Text slecture in English By Anonymous7
- Text slecture in English by Varun Vasudevan
- Video slecture in English by Nadra Guizani
- Video slecture in English by Jieun Kim
- Text slecture in Greek by Stylianos Chatzidakis
- Text slecture in Chinese by Weibao Wang
- Optimality of Bayes Rule
- Video slecture in English by Aaron Michaux
- Video slecture in Korean by Jeong-wan Kim
- Upper Bounds for Bayes Error
- Text slecture in English by G. M. Dilshan Godaliyadda
- Text slecture in English (includes the derivation of Chernoff Distance) by Jeehyun Choe
- Bayes Rule to Minimize Risk
- Video slectures in English, by Andy Park
- Text slecture in Chinese by Robert Ness
- Text slecture in English by Dennis Lee
- Bayes Rule for Normally Distributed Features
- Text slecture in English by Yanzhe Cui
- Text slecture in English by Jihwan Lee
- Bayes rule in practice
- Text slecture in English by Lu Wang
- Text slecture in English by Chuohao Tang
- Neyman-Pearson test and ROC curves
- Text slecture in English by Soonam Lee
- Video slecture in English by Jianxin Sun
- Text slecture in English by Hao Lin
3. Global (parametric) Density Estimation Methods
- Maximum Likelihood Estimation (MLE)
- Text slecture in English by Sudhir Kylasa
- Text slecture in English by Hariharan Seshadri
- Video slecture in English by Anantha Raghuraman
- Video slecture in English by Spencer Carver
- Text slecture in English by Lu Zhang
- Video slecture in English by Keehwan Park
- Text slecture in English by Wen Yi
- Text slecture in English by Zhenpeng Zhao
- Bayesian Estimation (BPE)
- Text slecture in English by Haiguang Wen
- Text slecture in English, by Shaobo Fang
- Text slecture in English by Yu Wang
4. Local ("non-parametric") Density Estimation Methods
- Introduction to Local density Estimation Techniques (so-called "non-parametric")
- Text slecture in English by Yu Liu
- Video slecture in Russian by Aziza Satkhozhina
- Video slecture in English by Nusaybah Abu-Mulaweh
- Video slecture in English by Chenxi Yuan
- Density Estimation with Parzen Windows
- Text slecture in English by Chiho Choi
- Text slecture in English by Ben Foster
- Text slecture in English by Abdullah Alshaibani
- Density Estimation with K-Nearest Neighbors (KNN)
- Text slecture in English by Raj Praveen Selvaraj
- Text slecture in English by Dan Barrett
- Video slecture in English by Qi Wang
- The Nearest Neighbor Decision Rule
- Text slecture in English by Sang Ho Yoon
- Text slecture in English by Jonathan Manring
5. Linear Classifiers
- Linear classifiers, projective coordinates, and Fisher linear discriminant
- Text slecture in English by John Mulcahy-Stanislawczyk
- Text slecture in English by Borui Chen
- Support Vector Machines (SVM)
- Text slecture in English by Xing Liu
- Video slecture in English by Tao Jiang
6. Supplementary Material
- Clustering Algorithms
- text slecture in English by David Runyan
Go to ECE662 Spring 2014 Course Wiki
Go to Slecture Page
