2014 Spring ECE 662 Boutin - Rhea

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

Course Information

Instructor:

• Professor Mimi Boutin

Office: MSEE342

Office hours

Assignment Drop Box

Lecture:

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

Slectures

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

• Slectures on Probability and Statistics
  • Whitening and Coloring Transforms, by Maliha Hossain
  • How to generate n-D Gaussian data in the two category case , by Minwoong Kim (in Korean)
  • Principal Component Analysis (PCA), by Tian Zhou
• Slectures on Curse of Dimensionality
  • Text slecture in English, and in Chinese by Haonan Yu
• Slectures on Bayes Rule
  • Bayes Rule in Layman's Terms
    • Link to page here
  • Derivation of Bayes Rule
    • Text slecture in English by Varun Vasudevan
      
    • Video slecture in English by Nadra Guizani
    • Video slecture in English by Jieun Kim
    • Derivation of Bayes rule_In Greek by Stylianos Chatzidakis
  • Bayes Rule for Normally distributed Features
    • Text slecture in English by Jihwan Lee
  • Upper Bounds for Bayes Error
    • Text slecture in English by G. M. Dilshan Godaliyadda
    • Text slecture in English by Yihan Ding PLEASE FOLLOW TEMPLATE
    • Text slecture in English (includes the derivation of Chernoff Distance) by Jeehyun Choe
  • Bayes Rule to Minimize Risk
    • Bayes Rule to minimize risk, by Andy Park
    • Bayes Rule for Minimizing Risk by Dennis Lee
  • Bayes rule in practice by Lu Wang
• Slectures on Neyman-Pearson test and ROC curves
  • Neyman-Pearson Lemma and Receiver Operating Characteristic Curve by Soonam Lee
• Slectures on Density Estimation
  • Maximum Likelihood Estimation (MLE)
    • Tutorial on Maximum Likelihood Estimates by Sudhir Kylasa
    • Maximum Likelihood Estimation Analysis for various Probability Distributions by Hariharan Seshadri
    • Expected Value and Deviation of Maximum LIkelihood Estimates over Multiple Trials by Spencer Carver
    • Deviation of Maximum Likelihood Estimators and Basic Properties of ML Method by Lu Zhang
    • Introduction to Maximum likelihood estimate by Anantha Raghuraman
  • Bayesian Estimation (BPE)
    • Link here
  • Introduction to Local density Estimation Techniques (so-called "non-parametric")
    • Introduction to local (nonparametric) density estimation methods by Yu Liu
  • Density Estimation with Parzen Windows
    • Parzen window method with cubic window - Text slecture in English by Chiho Choi
    • Link here
  • Density Estimation with K-Nearest Neighbors (KNN)
    • Link here
  • The Nearest Neighbor Decision Rule
    • Text slecture in English by Sang Ho Yoon
• Slectures on Linear Classifiers

Peer Reviews

• Instruction for peer reviewing HW1
• 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.

• Where to find data for HW1
• Possible Real-world data to use for class
• Programming help!
• New Discussion

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