Computer Science in Practice (67864)

Spring 2007

People

Lecturer:
Dani Lischinski (danix@cs.huji.ac.il)
Office: Ross 73
Office hours: Tuesday, 12:00-13:00
Office phone: 02-6585087

TA:
Amit Gruber ( amitg@cs.huji.ac.il)
Office: Ross 57
Office hours: Sunday, 16:00-17:00
Office phone: 02-6584875

Description

The course will provide an introduction to the types of problems and solutions that are common in applied fields of computer science. The goal will not be to cover the fundamentals of each applied field and the course is not a replacement for existing courses in vision, graphics and learning. Rather we will focus on a small number of problems in each field and give the students hands-on experience with algorithms for solving these problems. Among the problems considered will be visual tracking, colorization, dynamic range compression, clustering and regression. We will discuss a wide range of algorithms, unified by the fact that they all make use of basic linear algebra. Specifically, we will discuss numerical methods for solving sparse linear equations, singular value decompositions, recursive matrix inversion and the Kalman and Wiener filters.

Tentative Syllabus

1 Introduction and Google's page rank.

Part I - learning

2 Supervised learning - regression and the pseudoinverse
3 Supervised learning - classification and the Fisher Linear Discriminant
4 Unsupervised learning - dimensionality reduction using PCA

Part II - image processing and vision

5 Unsupervised learning - spectral segmentation.
6 Optical flow - Lucas Kanade
7 SFM - Tomasi Kanade

Part III - graphics

8 High dynamic range compression (Fattal, Lischinski, and Werman)
9 Graphical image manipulation: Colorization (Levin, Lischinski, and Weiss)
10 Methods for solving linear systems with large number of equations - Jacobi, SOR, conjugate gradient

Textbooks

Golub and Van Loan, Matrix Computations, Johns Hopkins, 1996 (3rd Ed.).

Kay S. M., Fundamentals of Statistical Signal Processing , Prentice Hall Signal Processing Series, 1993.

Grimmett and Stirzaker, Probability and Random Processes, Oxford press, 2001 (3rd Ed.) or 1992 (2nd Ed.).

Lecture notes:

Date	Subject	Files	Recommended Reading
February 27	Introduction and Google's page rank algorithm	CSIP2007-intro.pdf	-
March 6	Linear Regression	CSIP2007-regression.pdf	-
March 13	Linear Regression (cont.)	CSIP2007-regression part2.pdf	-
March 20	Classification and Fisher's Linear Discriminant	CSIP2007-classification.pdf	-
March 27	Unsupervised Learning and PCA	CSIP2007-PCA.pdf	-
May 15	Spectral Segmentation	CSIP2007-segmentation.pdf	-
May 29 / June 5	Structure From Motion	CSIP2007-sfm.pdf	-
June 5 / 12	Gradient-Domain HDR Compression	CSIP2007-hdrc.pdf	-
June 19	Colorization using Optimization	CSIP2007-colorization.pdf	-
June 26	Multigrid Methods	CSIP2007-MG.pdf	-
July 3	Optical Flow	CSIP2007-OF.ppt	-

Tirgul notes:

Date	Subject	Files	Recommended Reading
February 26	Octave programming	Tirgul1.ppt, tirgul1.m, meshgrid_example.m, my_repmat.m.	See Matlab/Octave links below
March 12	The Power Method	tirgul2.pdf	Matrix Computations (330-332)
March 19	Vector derivatives	tirgul3_derivatives.pdf	-
May 28	The Multivariate Normal Distribution	tirgul34.pdf	Grimmett, Kay (chapter 7)
June 4	Rotation Matrices	rotation.pdf	-
June 11	Structure From Motion	sfm.pdf	-
June 18	Deriving intrinsic images from image sequences	iccv01.pdf (the paper by Yair Weiss)	-
June 25	Iterative methods for sparse linear systems	iterative.pdf	Matrix Computations, pp. 510-513

Homework

There will be homework assignments about once every two weeks. There will be both theoretical exercises and practical exercises (using Octave).
Exercises should be submitted in pairs. If you discuss problems with another student, indicate on your writeup the name of your collaborator(s). In any case you must write up your own solutions.

Exercise	Subject	Submission date	Comments
ex1.ps ex1.pdf	The Power Method	Monday, March 19 2007	Slides and code examples from the first tirgul: Tirgul1.ppt, tirgul1.m, meshgrid_example.m, my_repmat.m, tirgul2.pdf.
ex2.ps ex2.pdf	Vector derivatives, linear regression, multi variate normal distribution	Thursday, April 26 2007	-
ex3.ps ex3.pdf	Linear Discriminant Analysis	Tuesday, June 5 2007	ex3_data_1, ex3_data_2, ex3_data_3 geig.m
ex4.ps ex4.pdf	3D Reconstruction (Tomasi-Kanade Factorization)	Friday, June 22 2006	alignMat.m
ex5.ps ex5.pdf	Deriving Intrinsic Images from Image Sequences	Friday, July 6 2006	iccv01.pdf, demo.m, zeroB.m, getAlbedo.m, reconsEdge3.m, invDel2.m solvePoisson.m show.m

Exams from previous years

Moed A 2006
Moed B 2006

Useful links

Computer Science in Practice (67864)

Spring 2007

People

Description

Tentative Syllabus

Textbooks

Lecture notes:

Tirgul notes:

Homework

Exams from previous years

Useful links

Programming

Linear algebra and applications

Learning

Clustering

Probability

Structure From Motion

Register,Grades,Submission