syllabus.md

layout	title	categories	lectures
default	Syllabus	schedule	date title who slides reading show 2017-07-25 Introduction; probability theory trevor 01_intro_prob_theory.pdf Bishop 1.1-1.2 1 date title who slides reading show 2017-07-27 Probabilistic models and parameter fitting trevor 02_statistical_schools.pdf Bishop 2.1*, 2.3*; 1.2.3-1.2.4 (* = first 2 pages) 1 date title who slides reading show 2017-08-01 Linear regression; Intro to regularisation andrey 03_linear_regression.pdf Bishop 3.1.1, 3.1.2, 3.1.4 1 date title who slides reading show 2017-08-03 Logistic regression classifier; Basis expansion andrey 04_logistic_regression.pdf Bishop 4.3.2, 3.1* 1 date title who slides reading show 2017-08-08 Iterative optimisation of loss functions; Model complexity and bias-variance analysis andrey 05_optim_regularisation.pdf Bishop 1.5.5, 3.2 1 date title who slides reading show 2017-08-10 Notes on vectors; Perceptron classifier andrey 06_vectors_perceptron.pdf Bishop 4.1.7 1 date title who slides reading show 2017-08-15 Artificial Neural Networks andrey 07_backpropagation.pdf Bishop 5.1, 5.2, 5.3 1 date title who slides reading show 2017-08-17 Artificial Neural Networks (cont.) andrey 08_convnet_autoenc.pdf Bishop 5.5.6 1 date title who slides reading show 2017-08-22 Support Vector Machines (hard margin) andrey 09_hard_margin_svm.pdf Bishop 7.1 1 date title who slides reading show 2017-08-24 Support Vector Machines (soft margin) andrey 10_soft_margin_svm.pdf Bishop 7.1.1 1 date title who slides reading show 2017-08-29 Kernel Methods andrey 11_kernel_methods.pdf Bishop 6.2 1 date title who slides reading show 2017-08-31 Ensemble Learning; Interim Summary andrey 12_ensemble_revision.pdf Bishop 14.2 1 date title who slides reading show 2017-09-05 Unsupervised Learning; Clustering andrey 13_clustering_gmm.pdf Bishop 9.1, 9.2 1 date title who slides reading show 2017-09-07 Expectation Maximisation Algorithm andrey 14_em_algorithm.pdf Bishop 9.4, 9.2.2 1 date title who slides reading show 2017-09-12 Principal Component Analysis; Multidimensional Scaling andrey 15_dimred_pca_mds.pdf Bishop 12.1, 12.4.3, Hastie 14.8 1 date title who slides reading show 2017-09-14 Manifold Learning; Spectral clustering andrey 16_manifold_learning.pdf Bishop 12.4.3, Hastie 14.5.3, 14.9 1 date title who slides reading show 2017-09-19 Bayesian inference trevor 17_bayesian_inference.pdf Bishop 2.3.6, 4.4-4.5 1 date title slides reading who show 2017-09-21 Bayesian inference (cont.) 18_bayesian_classification.pdf Bishop 2.1, 3.3, 3.4 trevor 1 date title 2017-09-25 Non-teaching week date title who slides reading show 2017-10-03 Probabilistic graphical models, fundamentals trevor 20_pgm_basics.pdf Bishop 8-8.3.1 1 date title who slides reading show 2017-10-05 Probabilistic graphical models, independence trevor 21_pgm_indy.pdf Bishop 8.2-8.3.2 1 date title who slides reading show 2017-10-10 Probabilistic graphical models, inference trevor 22_pgm_elimination.pdf Russell and Norvig Chapter 14 (see "Reading" on LMS) or Murphy 20.3 1 date title who slides reading show 2017-10-12 Probabilistic graphical models, statistical inference. Lecture cancelled, please listen to 2016 recording available under LMS in the reading/resources option. trevor 23_pgm_stat_infer.pdf Murphy 11.4.4 (generally dip into 11.3-11.4) 1 date title who slides reading show 2017-10-17 Probabilistic graphical models, hidden Markov models and message passing trevor 24_pgm_message_passing.pdf Murphy 17 (mainly 17.4-17.5) 1 date title slides who show 2017-10-19 Subject review 25_review.pdf trevor 1

Syllabus

We'll put the lecture slides in the week that we cover the material, as well as pointers to the required reading. Reading references to Bishop relate to the book Pattern recognition and machine learning by Christopher M. Bishop, 2006. Reading references to Murphy relate to the book Machine Learning : A Probabilistic Perspective by Kevin Murphy, 2014; available in electronic ebook form using the link.

{% for lect in page.lectures %} {% endfor %}

Date	Topic	Materials
{% if lect.type == "workshop" %} from {% endif %} {{ lect.date | date: "%a %-d/%-m" }}	{{ lect.title }} {% for matter in lect.materials %} {{ matter }} {% endfor %}	{% if lect.show == 1 %} {% if lect.slides %} Slides: {% assign first = 1 %} {% for matter in lect.slides %} {% if first == 1 %} {% assign first = 0 %} {% else %}   {% endif %} {{ matter }} {% endfor %} {% endif %} {% if lect.worksheet %} Worksheet: {% assign first = 1 %} {% for matter in lect.worksheet %} {% if first == 1 %} {% assign first = 0 %} {% else %}   {% endif %} {{ matter }} {% endfor %} {% endif %} {% if lect.reading %} Reading: {% assign first = 1 %} {% for matter in lect.reading %} {% if first == 1 %} {% assign first = 0 %} {% else %}   {% endif %} {{ matter }} {% endfor %} {% endif %} {% if lect.notebook %} Notebook: {% assign first = 1 %} {% for matter in lect.notebook %} {% if first == 1 %} {% assign first = 0 %} {% else %}   {% endif %} {{ matter }} {% endfor %} {% endif %} {% endif %} {% if lect.show == 2 %} {% if lect.reading %} <b>Reading: </b> {% assign first = 1 %} {% for matter in lect.reading %} {% if first == 1 %} {% assign first = 0 %} {% else %} <br> &nbsp; {% endif %} {{ matter }} {% endfor %} <br> {% endif %} {% endif %}

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