Notes: Lecture 1 Introduction
2009-10-26 Background, Python, MNIST data, and basic classification. Reading Homework Lecture 2 Nearest Neighbor Classification2009-11-02 Lecture Reading Homework - Homework 2 (due 9 Nov 2009; please hand in in class or exercises)
Lecture 3 Ellipses, Gaussians, Covariances
2009-11-09 Lecture Reading Homework - Homework 3 (due 16 Nov 2009; please hand in in class or exercises)
Lecture 4 Invariances; PCA2009-11-16 Lecture Homework Lecture 5 PCA, Statistical Foundations2009-11-23 Lecture Homework Lecture 6 2009-11-30 Lecture & Reading Homework Lecture 72009-12-07 Lecture & Reading Homework Lecture 82009-12-14 Lecture and Reading Homework Lecture 92010-01-11 Lecture and Reading Homework Lecture 102010-01-18 Lecture and Reading Homework - Optional: attempt to implement large margin perceptrons as described in this paper: Download
Lecture 112010-01-25 Lecture and Reading Homework Lecture 122010-02-01 Lecture and Reading Homework Lecture 132010-02-08 Lecture and Reading Homework |
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