Ay/Bi 199ab: Methods of Computational Science - Winter/Spring 2009

http://www.astro.caltech.edu/~george/aybi199/
Old class website (Fall 2007)

Times and location: Tue & Thu 1 - 2:30, in 100 Powell-Booth (CACR)

Special make-up lecture: Monday, May 18, 3 pm

Class coordinated by S. George Djorgovski, Rm. 221 Robinson Lab, ph. x-4415, email:

The plan of the lectures (may evolve)

The class blog
The class wiki

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Lectures :

• Apr. 2, 7: Semantic Web (Graham)
  • Matthew's slides, part I (pdf)
  • Matthew's slides, part II (pdf)

• Apr. 9: Best Programming Practices (Mahabal)
  • Ashish's slides (pdf)
    • Perl Best Practices (Damien Conway)
    • The Pragmatic Programmer (Andrew Hunt and Dvid Thomas)
    • International Obfuscated C Code Contest
    • C/CPP beautifiers
    • Comprehensive Perl Archive Network (CPAN)
    • Perltidy
    • Getop::Euclid
    • PerlPOD
    • Smart::Comments
    • Eiffel
    • Git
    • SubVersion (SVN)
    • Trac
    • perl
    • ruby
    • python
    • j
    • postscript
    • hotjava
    • UML
    • Jabber
    • vi
    • emacs
    • ddd
    • Benchmarking
    • Refactoring
    • 99 bottles of beer
    • Computer Language Benchmarks Game
    • Project Euler

• Apr. 14, 16: Matlab: introduction, scientific examples (Donalek)
  • Ciro's slides (pdf)
    • Examples: matlab_class_1.m * matlab_class.m * stat.m * coin_count.m
    • Matlab online Documentation
    • Matlab Primer (old but still good)
    • Netlab: Neural Network Package
    • SOM: Self Organizing Maps
    • Bayesian Network Toolbox

• Apr. 21, 23: Web Services (Williams)
  • Roy's slides, part I: Django (pdf)
  • Roy's slides, part II: Web services in the Cloud, and VO (pdf)
    • First reference for Django: Download, tutorial, documentation
    • Comprehensive book about Django
    • Cookbook and Wiki
    • Google Appengine Cloud Computing
    • Amazon Elastic Compute Cloud
    • Running Django on Google App Engine
    • Django on Google App Engine in 13 simple steps
    • Skyalert
    • US National Virtual Observatory

• Apr. 28: Bayesian Methods - A Refresher (Moghaddam)
  • Baback's slides (pdf)
    • Pattern Recognition and Machine Learning book on line
    • See also the links from Bayesian lectures in the first term, below

• May 5, 7: R package for statistics (Mahabal)
  • Ashish's slides, part I (pdf)
  • Ashish's slides, part II (pdf)

• May 12, 14: Mathematica (Pepke)
  • Shirley's Mathematica notebook, part I (nb file)
  • Shirley's Mathematica notebook, part II (nb file)
    • Mathematica online help
    • Wolfram tutorials
    • Wolfram demos

• May 18: Image processing (Cunha)
  • Alex's slides (pdf)
    • Netpbm package
    • ImageMagick
    • Matlab Image Processing Toolbox
    • Gimp (~ Photoshop)
    • ImageJ / Fiji

• May 26: Numerical libraries and tools (Petzold)
  • Linda's slides (pdf)

• May 28: Computational science and engineering (Stalzer)
  • Mark's slides (pdf)

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Lectures from the first term:

• Jan. 8: Introduction, logistics, wikis & blogs (Djorgovski, Mahabal)
  • George's slides (pdf)
    • e-Science activities at Caltech
    • J. Wing's ACM article "Computational Thinking" (2006): (local pdf file)
    • Extracts from the Nature v. 440, 23Mar06 issue, on e-Science (provided for the Caltech internal educational use only)
    • E. Dijkstra: "On the cruelty of really teaching computing science" (1988) (local pdf file)
    • Other useful links and readings
  • Ashish's slides (pdf)
    • Wikis:
      • Creole - common subset of most wikis
      • Twiki
      • Swiki
      • MediaWiki - used by Wikipedia
      • PmWiki
      • MoinMoin
    • Blogs:
      • Blogspot
      • Wordpress
      • LiveJournal

• Jan. 13: Flavors of languages (Mahabal, Stalzer)
  • Ashish's slides (pdf)
    • Pages on programming languages:
      • Wikipedia article on comparing programming languages
      • UMich CIS 400 course
      • Microsoft's wisdom
    • J. Hughes: "Why functional programming matters"
    • L. Allison's pages on Lambda calculus
    • J. Macdonald on Efficiency and Perl
    • Hamming's numbers in Perl * and in Prolog
    • Computer language benchmarks game
    • Geek humor:
      • How to shoot yourself in the foot
      • If languages were religions
      • 99 bottles of beer
  • Mark's slides (pdf)
    • Wikipedia article on Programming Language Theory
    • IBM Cell Broadband Engine Architecture
    • op 500 list of world's fastest computers
    • The Java HotSpot Performance Engine Architecture
    • Ct: C for Throughput Computing

• Jan. 15, 20, 22: Intro to Python for scientific applications (Aivazis)
  • Michael's slides (pdf)
  • www.python.org
  • docs.python.org

• Jan. 27, 29, Feb. 3: Databases (Graham)
  • Matthew's slides, part I (pdf)
  • Matthew's slides, part II (pdf)
  • Matthew's slides, part III (pdf)

• Feb. 5, 10, 12: Scientific visualization (Lombeyda)
  • Santiago's slides, part I (pdf)
  • Santiago's slides, part II (pdf)
  • Santiago's slides, part III (pdf)
  • Some visualization packages:
    • R-project
    • Rserve
    • Mondrian
    • Molegro
    • XMDV
    • Tableau
    • Topcat

• Feb. 17: Introduction to data mining (Djorgovski)
  • George's slides (pdf)
  • Kirk Borne's slides shown by George (pdf)
  • Some useful links:
    • Gregory Piatetsky-Shapiro's KDNuggets (LOTS of good links)
    • Andy Pryke's The Data Mine
    • ACM's Special Interest Group on Knowledge Discovery in Databases
    • ... and their newsletter, "Explorations"
    • Andrew Moore's statistics and data mining tutorials
    • The Classification Society of North America (CSNA)
    • Weka package and links
    • David Dowe's mixture modeling links
    • Fionn Murtagh's multivariate data analysis software and links
    • StatLib at CMU
    • StatCodes at PSU
    • IAPR Pattern Recognition Education Resources
    • VOStat @ PSU
    • VOStat @ Caltech
    • SAMSI Workshops and Tutorials

• Feb. 19: Unsupervised classifiers (Donalek, Graham)
  • Ciro's slides (pdf)
  • Matthew's slides (pdf)

• Feb. 24: Supervised classifiers (Donalek)
  • Ciro's slides (pdf)
    • FANN (Fast Artificial Neural Networks)
    • Netlab Toolbox (Matlab)
    • SOM Toolbox (Matlab)
    • SVM library LIBSVM
    • VONeural/DAME project
  • A few links about Decision Trees:
    • Wikipedia article (general)
    • Wikipedia article (DT learning)
    • An application in astronomy: star-galaxy classification for the DPOSS survey

• Feb. 26: Introduction to Bayesian methods (Mahabal)
  • Ashish's slides (pdf)
    • Bayesian Network Toolbox (for Matlab)
    • "Naive Bayesian" tutorial
    • Example: three prisoners problem
    • Example: Monty Hall problem
  • More statistics and Bayesian links from George:
    • Center for Astrostatistics @ PSU
    • Statistics bibliography @ PSU
    • E.T. Jaynes's book "Probability Theory: The Logic of Scicence" * Here is a local copy (pdf)
    • More Bayesian links

• March 3: Bayesian data modeling and analysis (Jewell)
  • Jeff's slides (pdf)
  • More Bayesian links from Jeff:
    • Tom Loredo's BIPS (Bayesian Inference for the Physical Sciences) - lots of good links and resources
    • The Inference Group at Cavendish
    • David MacKay's book "Information Theory, Inference, and Learning Algorithms"
    • Markov Chain Monte Carlo (MCMC) preprint server
    • MCMC tutorial from UCLA

• March 5: Nonparametric Bayes and Gaussian Processes (Moghaddam)
  • Baback's slides (pdf)
  • More true Bayesian religion from Reverend Baback:
    • C. Rasmussen & K. Williams, "Gaussian Processes for Machine Learning" (book download)
    • M. Lavine, "What is Bayesian statistics and why everything else is wrong" * local pdf file
    • S. Goodman, "Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy", Ann. Intern. Med., 130, 995 (1999)
    • S. Goodman, "Toward Evidence-Based Medical Statistics. 2. The Bayes Factor", Ann. Intern. Med., 130, 1005 (1999)
    • S. Fienberg, "When Did Bayesian Inference Become 'Bayesian'?", Bayesian Analysy, 1, 1 (2006) (a historical account about the rise of the Bayesians...)
    • A. Gelman's blog "Statistical Modeling, Causal Inference, and Social Science"
    • Baback also recommends this book as an excellent *short* Bayesian primer

• March 10: Graphical models and Bayes networks (Heckerman)
  • David's slides (pdf)
  • Dr. Heckerman's webpage (contains links to BN tutorials and science examples)
  • BN Learning Tutorial (local pdf file)

Last changed: 22 June 09, SGD