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 & Na�ve Bayes
Coursework Handout
laplace_demo.m
logistic_classification_demo.m
lab_4_sol.m
na�ve_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
