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Lectures
Classification with Normal Densities
Ellipses, Gaussians, Convariances
Introduction
Invariances, PCA
Kernel Density Estimation
kNN Methods
Multilayer Perceptrons
Nearest Neighbor Classification
Parameter Estimation
PCA, Statistical Foundations
Perceptrons and Logistic Regression
Support Vector Machines
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Lectures
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Introduction
Lecture 1 Introduction
2009-10-26
Background, Python, MNIST data, and basic classification.
Introduction to Pattern Recognition
patrec_-_Introduction_to_Python_--_Sage.pdf
patrec_-_The_Seven_Segments_Example_--_Sage.pdf
patrec_-_MNIST_Data_Worksheet_--_Sage.pdf
Reading
k-nearest neighbors
skim ESLI Chapters 1 & 2
Homework
Homework 1
(due 4 Nov 2009)
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