Dr. Brian McWilliams, Dr. Aurelien Lucchi - Autumn Semester, 2014
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18 Dec: Added solutions for SSVM exercises.
17 Dec: Added solutions for LBP exercises.
11 Dec: Added homework for SSVM lecture.
10 Dec: Added SSVM slides.
10 Dec: Added solutions for SVM and CRF lectures.
4 Dec: Added LBP exercises and solutions for factored Gaussians example.
3 Dec: Added SVM slides.
3 Dec: Added homework for SVM lecture.
26 Nov: Added CRF slides.
26 Nov: Added homework for CRF lecture.
17 Nov: Added Sampling slides.
5 Nov: Added solutions for belief nets and belief prop and reading for Loopy BP.
27 Oct: Added homework for belief prop and Variational slides.
22 Oct: Added solutions for Holmes/Watson network and another inference exercise.
22 Oct: Added solutions for homework 5 and 6.
13 Oct: Added solutions for homework 4.
2 Oct: Added lecture 3 slides and additional exercises for lecture 1.
23 Sept: added more reading for lecture 1.
FAQs section added.
2014 course website updated.
This course will focus on inference with statistical models for image analysis. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields. We apply the approach to traditional vision problems such as image denoising, as well as recent problems such as object recognition. The course covers amongst others the following topics:
| Lectures | Monday, 15:00-16:00 |
CAB G 51 |
| Thursday, 10:00-12:00 |
CLA E 4 |
30 Minute oral exam in English.
| Day | Lecture Topics | Lecture Slides | Additional Exercises | Reading | Background Material |
| Sep 18 | Introduction/Learning from Data | Lecture 1 | hw solutions: p1, p2, p3, p4 | Barber Ch. 1 , |
notes
on machine learning probability background |
| Sep 22 | Introduction/Learning from Data (cont.) | Learning from data basics (solutions), | Barber Ch. 1 , 8, 13.2 , 17.1, 18.1.1 | ||
| Sep 25 | Probabilistic models | Lecture 2 | hw solutions: p1, p2 | Barber Ch. 8, 10 |
Ghahramani on Bayesian modeling Nice example of a generative model |
| Sep 29 | Probabilistic models | Barber Ch. 17.4, 29.3-5 | |||
| Oct 02 | Belief Networks | Lecture 3 |
worked example solutions
Inference in Belief nets (solutions) |
Barber Ch. 2, 3 |
|
| Oct 09 | Markov Random Fields | Lecture 4 |
hw4 solutions |
Barber Ch. 4 | |
| Oct 16 | Learning as Inference | Lecture 5 |
hw5 solutions |
Barber Ch. 9 | |
| Oct 16 | MAP inference |
Lecture 6 |
hw6 solutions |
Barber Ch. 9, 28.9 |
1. energy minimization via graph-cuts 2. texture synthesis 3. photomontage |
| Oct 23 | Belief Propagation |
Lecture 7 |
Barber Ch. 5 | ||
| Oct 27 | Belief Propagation (cont.) Variational Approximation |
Lecture 8 |
Belief-prop homework (solution) |
Barber Ch. 18.2.2, 28 |
|
| Nov 6 | Variational Approximation (cont.) Loopy Belief Propagation |
Lecture 9 |
Additional exercises Solution to factored Gaussians. LBP exercises (solutions) |
Barber Ch. 28 Barber 28.7 Wainwright and Jordan 3-4.1.6 |
Challis and Barber. Gaussian Kullback-Leibler Approximate Inference |
| Nov 17 | Sampling |
Lecture 10 |
Barber Ch. 27 | ||
| Nov 27 | Conditional Random Fields |
Lecture 11 |
series11.pdf solutions11.pdf hw11 solutions |
Barber 9.6.5 and 23.4.3 |
Intro to CRFs Application to image segmentation Learning CRFs with graph cut |
| Dec 1 | No class | ||||
| Dec 4 | SVMs |
Lecture 12 |
series12.pdf solutions12.pdf |
SVM tutorial |
Learning the kernel Discriminative MRFs |
| Dec 11 | Structured SVMs |
Lecture 13 |
series13.pdf solutions13.pdf |
||
| Dec 15 | No class |
D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press 2012.
The main course text. Brand new book which covers many topics in graphical models and machine learning.
Available for free from here.
M. Wainwright and M.I. Jordan. Graphical models, exponential families and variational inference. Foundations and Trends in Machine Learning 2008.
Advanced treatment of graphical models and variational inference. Available free from here.
David J.C. Mackay. Information Theory, Inference and Learning
Algorithms. Cambridge University Press, 2003.
Available for free from here.
C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.
This is an excellent introduction to machine learning
that covers most topics which will be treated in the lecture. Contains
lots of exercises, some with exemplary solutions.
D. Koller and N. Friedman. Probabilistic Graphical Models:
Principles and Techniques. The MIT Press 2009.
Covers Bayesian networks and
undirected graphical models in great detail.
Q: What is a good reference for probability theory required for the course?
A: See Barber Ch. 1. and MacKay: Ch. 2, 3. Make sure you are comfortable with the exercises in the first week's slides too.
Q: What is the scope of the course?
A: We cover material from Part I (all), II and III (some) and V (all) of Barber. We look briefly at the first four sections of Wainwright & Jordan.
