• 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

• Michael's slides (pdf)
• www.python.org
• docs.python.org

• Matthew's slides, part I (pdf)
• Matthew's slides, part II (pdf)
• Matthew's slides, part III (pdf)

• 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

• 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

• Ciro's slides (pdf)
• Matthew's slides (pdf)

• 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

• 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

• 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

• 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

• David's slides (pdf)
• Dr. Heckerman's webpage (contains links to BN tutorials and science examples)
• BN Learning Tutorial (local pdf file)

• Matthew's slides, part I (pdf)
• Matthew's slides, part II (pdf)

• 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

• 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

• Baback's slides (pdf)
  • Pattern Recognition and Machine Learning book on line
  • See also the links from Bayesian lectures in the first term, below

• Ashish's slides, part I (pdf)
• Ashish's slides, part II (pdf)

• Shirley's Mathematica notebook, part I (nb file)
• Shirley's Mathematica notebook, part II (nb file)
  • Mathematica online help
  • Wolfram tutorials
  • Wolfram demos

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

• Linda's slides (pdf)

• Mark's slides (pdf)

An even older class website (Fall 2007)