Machine Learning Module ML(M)

Semester 2, 2007

Lecturer: Professor. M. A .Girolami

Lectures:

Monday, 1.00pm, Maths Building (204)

Thursday, 1.00pm, Maths Building 515 (4B)

Laboratory & Tutorial:

Friday, 1.00pm, Boyd Orr, Level 4 Laboratory

Module Descriptor

Student Ratings of Module 2006

I feel I learned a lot from the course and it pushed me! - Robin Donaldson (Level 4 CS, 2006)

Altogether I really enjoyed the course and I learned a lot of useful techniques.

I really liked your lectures especially the tutorials and scripts - Clement Rodegast (Exchange Student CS, 2006)

Week No.	Lecture Slides	Lecture Notes	Lab Sheet	Tutorial	Additional Files
1.	Lecture 1 Lecture 2	Introduction & Linear Regression	Linear Regression	Vector Differentiation	wk_1_matlab.m long_jump_data.txt
2.	Lecture 1 Lecture 2	Generalisation	Cross Validation		cross_val.m cv_demo.m long_jump_cv.m
3.	Lecture 1 Lecture 2	Probabilistic & Bayesian Methods	Bayesian Regression		max_like_demo.m brdemo.m regdemo.m wk3_lab_1_sol.m wk3_lab_2_sol.m cross_val_wk3.m gauss.m kernel_func.m
4.	Lecture 1 Lecture 2	Probabilistic Classification Methods	Laplace Approximation, Logistic Regression & Naïve Bayes	Coursework Handout	laplace_demo.m logistic_classification_demo.m lab_4_sol.m naïve_bayes_binary.m 20news_w100.mat rip_dat_tr.txt rip_dat_te.txt
5.	Lecture 1 Lecture 2	Non-Probabilistic Classification Methods	Support Vector Machines & K-Nearest Neighbours		knn_multi_class.m svm_demo.m svm_demo_kernels.m monqp0.m cout.m cross_val_wk5.m digits_3_8.mat
6.	Lecture1&2	Probability Density Estimation	EM Algorithm		gauss_density_est.m gauss_mix_em_demo.m mix_gauss_density.m multi_var_gauss_sampler.m Gauss_Mix_Data.mat Lab_6_EM_Data.mat
7.	Lecture1&2	Principal Component Analysis	PCA on images		olivettifaces.mat faces_demo.m power_pca.m
8.	Lecture 1	Cluster Analysis	Image segmentation & nonlinear clustering	Girolami, M., Mercer kernel-based clustering in feature space, IEEE Trans NN, 13(3), 780-784, 2002.	kmeans.m kernel_kmeans.m wk8_demo_1.m wk8_demo_2.m wk8_demo_dat.mat kernel_func.m ollivettifaces.mat wee_dog.jpg water_lillies.jpg
9.		Independent Component Analysis	Noisy_Images		ICA Research Network
10.		Tutorial		Coursework Submission

Student Resources

Matlab Tutorials

1. http://www.mathworks.com/academia/student_center/tutorials/launchpad.html
2. http://www.duke.edu/~hpgavin/matlab.html
3. http://www.mathworks.com/access/helpdesk/help/techdoc/learn_matlab/learn_matlab.shtml

Suggested Books

1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction., Hastie, Tibshirani & Friedman, Book Website
2. Pattern Classification, 2^nd Edition., Duda, Hart & Stork, Book Website
3. Pattern Recognition & Machine Learning ., Bishop, Book Website

Data Repositories

1. UCI Data Repository All the standard data collections used to illustrate various Machine Learning methods are found here
2. DELVE Datasets for Evaluating and Comparing Learning Methods

Useful Reference Material

1. Matrix Cookbook An encyclopaedic collection of just about every matrix and vector equality you will ever need
2. Gaussian Identities Sam Roweis compiled a useful listing of some of the more common Gaussian identities you may encounter
3. Nice Wikpedia entry for Machine Learning.