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LECTURE MATERIAL The lecture material will appear here, week by week as it is produced. Notes will be handed out in the lectures.Also I will hand out copies of the handwritten overheads presented during the lectures. If you miss a lecture, please download the notes from here, or copy them from someone else (ie. don't bug me!). Introduction Supplementary mathematics : vector and linear algebra Introduction to MATLAB Preliminaries:Data and Mathematics Nearest Neighbour Classification Linear Dimension Reduction My eigpca.m MATLAB code for computing PCA eigenvectors (even in high dimensional spaces). Density Estimation Demos and stuff Age/Education/Hours demo : you need the files age_education.m and age_education_data.xlsNearest Neighbour demo : you need the files nearest_neighbour_demo.m and nearest_neighbour.m PCA demo : pca_demo.m See the links page for the Eigenfaces stuff. BOOKS
No single book covers the material for the course. Fairly detailed lectures notes will be provided for the course. However you may find it useful to refer to the texts below for different presentations of some of the material. I do not recommend that you buy these books. Machine
Learning by Tom Mitchell. Here is a
list of errata
for the book obtained from Tom Mitchell's pages.
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