INF5300 - V?r 2016

Undervisningsplan

Note: This plan is subject to change!

Dato	Undervises av	Sted	Tema	Kommentarer / ressurser
20.01.2016	Anne Solberg Are Jensen	OJD, 2458 Postscript	Introduction and a taste of the course. Classification basics (reminder).	Introduction to the course (lecture foils) Feature-based classification principles (lecture foils)
27.01.2016	Are Jensen		Feature selection (in the context of supervised classification)	Lecture foils (4pp). Reading material: Sections 5.1, 5.2, 5.5 (5.5.1 and 5.5.2 not too detailed), 5.6 in ”Pattern Recognition” by S. Theodoridis and K. Koutroumbas. For the randomized methods: Computational methods of feature selection, Liu, 2007, chapter Randomized feature selection, especially sections 6.4 and 6.5.
03.02.2016	Are Jensen		Linear feature transforms	Lecture foils (4pp). Reading material: The chapter on PCA in C.R. Shalizi's "Advanced Data Analysis from an Elementary Point of View". (Until the example in 17.2.) The section on LDA and Fisher's reduced rank LDA, 4.3, in The Elements of Statistical Learning, Hastie et al. Supporting material: Short appendix on Lagrange multipliers, from PRML, Bishop 2006. The following very elementary introduction to PCA might be useful for some: A tutorial on PCA, Shlens, 2009.
10.02.2016	Are Jensen	DSB lab, room 4270	Lab on feature selection and linear transforms	Lab exercises Script on the "curse of dimensionality" Feature selection example script Helping you along: Example code solving parts of the PCA exercise
17.02.2016	Anne Solberg		Regularization, and snakes - active contour models	Lecture foils Supporting material: Reading material: 3.7.1 and 5.1.1 i Szeliski 6.1-6.3 in Nixon and Aguado ~inf5300/pensum-artikler/activecontour_kap6.pdf
24.02.2016	Anne Solberg		Markov random fields and contextual models	Lecture notes Reading material: 3.7.2 and 5.3 in Szeliski. Additional reading: Will use the notation from ”Random field models in image analysis” by Dubes and Jain, Journal of Applied Statistics, 1989, pp. 131-154, except section 2.3 and 2.4.
02.03.2016	Anne Solberg		Lab on active contours/Markov models	Exercise text
09.03.2016	Are Jensen		Basics of support vector machine (SVM) classification	Lecture notes Based on the following sections from Pattern Recognition by Teodoridis/Koutroumbas found at ~inf5300/pensum-artikler: svm_kap3.pdf svm_kap4.pdf svm_appendix.pdf
16.03.2016	Are Jensen		SVM lab	Lab exercises inf5300_2016_svmLab.zip
Easter				Mandatory exercise Deadline April 22
30.03.2016	Anne Solberg		Extracting good features for matching/trackingLe	Lecture notes Curriculum: Chapter 4 in Szeliski. SIFT paper by Lowe Lab on feature extraction
06.04.2016	Anne Solberg		Image alignment and RANSAC	Lecture notes Reading material: Background on gemetric transforms: 2.1.1 and 2.1.2 in Szeliski Ransac: 6.1 More on Ransac: Ransac for dummies
13.04.2016	Anne Solberg		Motion estimation	Lecture notes Lab on motion is here
20.04.2016	Anne Solberg		Lab	Solve labs on SIFT and motion or work with mandatory exercise
27.04.2016				No lecture this week.
04.05.2016				No lecture this week.
11.05.2016	Are Jensen		Basics of graph-based semi-supervised learning	Lecture slides (some slight changes made May 12) Reading material: Chapelle et al. SSL chapter one. Bengio et al. Label propagation. Note: Please see page 1 on the lecture slides to get a detailed description of what is the curriculum in the above links! Supporting material: Chapter 1-4 of X. Zhu. PhD thesis, 2005.
18.05.2016	Are Jensen		Lab on graphs and segmentation	Lab exercises INF5300_2016_LPBasedImageSegmentation.zip
08.06.2016	Anne Solberg Are Jensen		Repetition	Note the updated date!
15.06.2016		Room Shell (1456)		The schedule for the exam day is sent by e-mail.

Publisert 13. jan. 2016 15:00 - Sist endret 13. juni 2016 15:58