Ay/Bi 199ab: Methods of Computational Science - Winter/Spring 2011

Open to everyone interested

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

Class coordinated by Mary Kennedy, rm. 246 Beckman Lab, ph. x-3923, email:
S. George Djorgovski, rm. 208 Cahill, ph. x-4415, email:

Winter 2011:

The class was offered as Bi 199a, and it is mainly about learning Matlab, but also with some image processing and visualization. Here is the syllabus (pdf)

Spring 2011:

Joint Ay/Bi lectures, covering other topics in computational science, of a broad interest.

This page: http://www.astro.caltech.edu/~george/aybi199/

The plan of the lectures (will evolve)

The class blog
The class wiki

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Lectures and links (subject to updates):

• March 28: Introduction, logistics, wikis & blogs (Djorgovski, Mahabal)
  • George's slides (pdf)
    • e-Science activities at Caltech
    • Download a free copy (pdf) of the book The Fourth Paradigm
    • 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)
    • A history of silicon valley
    • Some other useful links and readings
    • AstroInformatics2010 conference, with lots of good talks: slides (pdf) * podcasts/webcasts
  • Ashish's slides (pdf)
    • Wikis:
      • Creole - common subset of most wikis
      • Twiki
      • Swiki
      • MediaWiki - used by Wikipedia
      • PmWiki
      • MoinMoin
    • Blogs:
      • Blogspot
      • Wordpress
      • LiveJournal

• March 30: Best Programming Practices (Mahabal)
  • Ashish's slides (pdf)
    • The Pragmatic Programmer (Andrew Hunt and Dvid Thomas)
    • C/C++ beautifiers * vi * gnu emacs * ddd
    • Perl * Perl Best Practices (Damien Conway) * Comprehensive Perl Archive Network (CPAN) * Perltidy * Getop::Euclid * PerlPOD * Smart::Comments
    • python * j * postscript * hotjava * UML * Jabber * ruby * Eiffel * Git
    • SubVersion (SVN) * Trac
    • Benchmarking * Refactoring
    • Computer Language Benchmarks Game * Project Euler * International Obfuscated C Code Contest

• Apr. 5: Introduction to data mining (Djorgovski)
  • George's slides (pdf)
  • Kirk Borne's slides shown by George (pdf)
  • "Data Mining and Machine Learning in Astronomy", N. Ball & R. Brunner, Int. J. Mod. Phys. D, 19, 1049 (2010) (local pdf) * (arXiv link)
  • 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
    • Pattern Recognition and Machine Learning book on line
    • StatLib at CMU
    • StatCodes at PSU
    • IAPR Pattern Recognition Education Resources
    • VOStat @ PSU

• Apr. 7: Flavors of languages (Mahabal)
  • 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

• Apr. 12 and 14: Machine learning and automated classifiers (Donalek)
  • Ciro's slides (pdf) (both lectures)
    • 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

• Apr. 19 and 21: Scientific visualization (Lombeyda)
  • Santiago's slides, part I (pdf)
  • Santiago's slides, part II (pdf)
  • Santiago's links
  • Some visualization packages:
    • R-project
    • Rserve
    • Mondrian
    • Molegro
    • XMDV
    • ParaView
    • Tableau
    • Topcat
    • LLNL VisIt
    • Processing
    • Blender
    • ProtoVis
    • Splatting
    • Worldle
    • Radial map of journal citations
  • Additional links:
    • Caltech Library link to the on-line book "Visualizing Data", by Ben Fry
    • InfoVis Wiki
    • "Design Principles for Visual Communication", M. Agrawala, W., Lee & F. Berthouzoz, Comm. ACM, v54, p60 (April 2011) [local pdf]

• Apr. 26: Towards the Exascale computing (Kogge)
  • Peter's slides (pdf)
  • Peter's website
  • DARPA report "ExaScale Computing Study: Technology Challenges in Achieving Exascale Systems", Kogge et al. (Sept. 2008) [local pdf]

• Apr. 28: Parallel computing (Upchurch)
  • Ed's slides (pdf)

• May 3 and 5: Databases (Graham)
  • Matthew's slides (pdf) Part I * Part II

• May 10: Semantics (Graham)
  • Matthew's slides (pdf) Matthew's slides

• May 12: 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

• May 17: Introduction to Bayesian methods (Moghaddam)
  • Baback's slides (pdf)
    • 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 Analysis, 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

• May 19 and 24: R package for statistics (Mahabal)
  • Ashish's slides, part I (pdf)
  • Ashish's slides, part II (pdf)
  • Graphics page
  • Some data sets used:
    • http://www.diamondse.info
    • http://fueleconomy.gov
    • http://uk.imdb.com/interfaces

• May 26: Computational Science and Engineering (Stalzer)
  • Mark's slides (pdf)

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Last changed: 26 May 2011, SGD