A First Course in Machine Learning

Simon Rogers and Mark Girolami: Accompanying material

Chapman & Hall/CRC, ISBN-13: 978-1439824146

Matlab demos

Chapter 1

• plotlinear.m
• fitlinear.m
• fitolympic.m
• olymppred.m
• synthquad.m
• olymppoly.m
• olympval.m
• cv_demo.m
• regls.m

Chapter 2

• approx_expected_value.m
• gauss_surf.m
• genolymp.m
• olymplike.m
• predictive_variance_example.m
• w_variation_demo.m

Chapter 3

• coin_scenario1.m
• coin_scenario2.m
• coin_scenario3.m
• olympbayes.m
• margpoly.m

Chapter 4

• logmap.m
• lapexample.m
• loglap.m
• randwalks.m
• mhexample.m
• logmh.m

Chapter 5

• bayesclass.m
• newspred.m
• nonlinlogreg.m
• knnexample.m
• knncv.m
• svmhard.m
• svmsoft.m
• svmgauss.m
• svmroc.m

Chapter 6

• kmeansexample.m
• kmeansK.m
• kernelkmeans.m
• mixgen.m
• gmix.m
• gmixcv.m
• binmix.m

Chapter 7

• pcaexample.m
• pcaexample2.m
• ppcaexampls.m
• ppcamvexample.m
• mpvis.m

Chapter 8

• gpprior.m
• gppred.m
• gpprednoise.m
• gpclass.m
• class2d.m
• gpclasslaplace.m
• gphyper.m

Chapter 9

• gibbsgauss.m
• gibbs_gp_class.m
• state_chain.m
• sampleworld.m
• convergence.m
• badmh.m
• abc.m
• gp_smc.m