COS 598D, Spring 2006: Home Page
Princeton University
Computer Science Department

Computer Science 598D
Data Analysis and Modeling in Science, Engineering and Information Services

Jaswinder Pal Singh

Spring 2006

Directory
General Information

Course Summary

Many areas of science and information technology are producing tremendous amounts of data at dramatically higher rates than ever before, as a result of innovations in observational equipment, the advent of the World Wide Web, and the continued exponential growth of computational capabilities. The avalanche of data has created the exciting opportunity for new scientific discovery and insights through data analysis, which is quickly joining simulation as a key component of the third, computational, pillar of science. It has also driven the development of novel information services with powerful societal impact. However, truly taking advantage of these opportunities requires an interdisciplinary approach, bringing together data analysis methods, dynamic simulation models, applications of the methods to real-world problems, scalable systems, and their interplay.

Bringing together faculty with relevant expertise from several different departments, this cross-disciplinary course provides an introduction to many of the key modern methods for data analysis, and their specializations to and applications across several disciplines ranging from biology to astrophysics to analysis of text, audio and video. It also discusses the applicability of key approaches to different application areas, exposing students to applications of the methods in a variety of disciplines, to foster shared learning and expose new challenges. And it discusses the interplay of data analysis with dynamic simulation and model analysis, which is increasingly critical in many areas, as well as with scalable computing as a vehicle for performing sophisticated analyses on large data sets.

The course is appropriate for students from many departments who want to learn about methods and their applications, including Computer Science students as well as other students from science and engineering disciplines. Audits are welcome. 

 
Interested in attending? Contact Prof. Jaswinder Pal Singh at jps@cs.princeton.edu

Click here for Syllabus and Course Materials

Useful Links

Boosting http://www.cs.princeton.edu/~schapire/boost.html
SVD Matlab, IMSL Libraries, LAPACK
PCA http://nru.dk/bbtools/help/toolbox/bbtools/ex_pca.html
(Modification of SVD for dynamical systems) http://www.cam.cornell.edu/~rclewley/research.html
Independent Components Analysis (ICA) http://www.cis.hut.fi/projects/ica/fastica/code/dlcode.html
Clustering http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=7486
Locally Linear Embedding http://www.cs.toronto.edu/~roweis/lle/
ISOMAP http://isomap.stanford.edu/
Random Projections http://theory.stanford.edu/~rajeev/cs361-2003.html
Hidden Markov Models http://lib.stat.cmu.edu/
Confidence Intervals for Random Variables Matlab, IMSL, http://lib.stat.cmu.edu/
ISA http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PLEEE8000067000003031902000001&idtype=cvips&gifs=yes
  (can be implemented using standard Matlab or IMSL libraries)
Bayesian Networks http://bnt.sourceforge.net/
  http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml
  http://vibes.sourceforge.net/
  http://www.r-project.org/
Support Vector Machines (SVM) http://theoval.sys.uea.ac.uk/~gcc/svm/toolbox/
  http://www.isis.ecs.soton.ac.uk/resources/svminfo/download.php

 

Administrative Information

Lectures: M 1:30-4:00 p.m., Room: CS 105 (Small Auditorium)

Professor: Jaswinder Pal Singh - 423 CS Building - 258-5329 jps@cs.princeton.edu

Graduate Coordinator: Melissa Lawson - 310 CS Building - 258-5387 mml@cs.princeton.edu

Coordinator: Steven Kleinstein - stevenk@cs.princeton.edu

Teaching Assistant: Christopher Calderon - ccaldero@princeton.edu