Lectures (Old)

Notes:

Lecture 1 Introduction

2009-10-26

Background, Python, MNIST data, and basic classification.
    Reading
    Homework

    Lecture 2 Nearest Neighbor Classification

    2009-11-02

    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; PCA


    2009-11-16

    Lecture
    Homework

    Lecture 5 PCA, Statistical Foundations

    2009-11-23

    Lecture
    Homework

    Lecture 6 

    2009-11-30

    Lecture & Reading
    Homework

    Lecture 7


    2009-12-07

    Lecture & Reading
    Homework

    Lecture 8

    2009-12-14

    Lecture and Reading
    Homework

    Lecture 9

    2010-01-11

    Lecture and Reading
    Homework

    Lecture 10


    2010-01-18

    Lecture and Reading
    Homework
    • Optional: attempt to implement large margin perceptrons as described in this paper: Download

    Lecture 11

    2010-01-25

    Lecture and Reading
    Homework
    • None

    Lecture 12

    2010-02-01

    Lecture and Reading
    Homework
    • None

    Lecture 13


    2010-02-08

    Lecture and Reading


    Homework

    • None
    Ċ
    02-nnbr.pdf
    (560k)
    Thomas M. Breuel,
    Nov 2, 2009, 5:07 AM
    v.5
    Ċ
    04-nnbr.pdf
    (178k)
    Thomas M. Breuel,
    Nov 15, 2009, 10:10 PM
    v.5
    Ċ
    05-nnbr.pdf
    (140k)
    Thomas M. Breuel,
    Nov 22, 2009, 10:31 PM
    v.5
    Ċ
    LargeMarginsUsingPerceptron.pdf
    (191k)
    Thomas M. Breuel,
    Jan 21, 2010, 11:24 PM
    v.5
    Ċ
    decision-boundaries-normal.pdf
    (279k)
    Thomas M. Breuel,
    Dec 6, 2009, 9:11 PM
    v.5
    Comments