Stephan Paukner :: syslog

• David MacKay: Information Theory, Pattern Recognition and Neural Networks: The Book

  tags: machinelearning books

  • Information Theory, Inference, and Learning Algorithms
  • 640 pages, Published September 2003
  • PDF (A4) pdf (9M) (fourth printing, March 2005)

• Cookbook for R » Cookbook for R

  HTML

  tags: r statistics programming

  • formerly named R Cookbook
  • It is not related to Paul Teetor’s excellent R Cookbook

• Big Data -- Why the 3Vs Just Don’t Make Sense -- TDWI -The Data Warehousing Institute

  When are we done defining big data? p.1

  tags: datascience bigdata opinion

  • settled on 3 Vs -- volume, variety, and velocity
  • another V: value
  • if big data is understood solely on the basis of these trends, it isn’t clear that it’s at all hype-worthy
  • if “big data” simply describes the volume, variety, and velocity of the information that constitutes it, our existing data management practices are still arguably up to the task
  • big data is hyped on the basis of its real or imagined outputs
  • a lot more interesting when you bring in ‘V’ for value

• No, Really, Some of My Best Friends Are Data Scientists

  When are we done defining data science?

  tags: datascience statistics opinion

  • the skills of a “data scientist” are those of a modern statistician
  • know how to move data around and manipulate data with some programming language
  • know how to draw informative pictures of data
  • Knowledge of stats, errorbars, confidence intervals
  • try to get people from different backgrounds
  • Great communication skills
  • a lot of what we teach The Kids now looks a lot more like machine learning than statistics as it was taught circa 1970, or even circa 1980
  • Everything I know about statistics I’ve learned without formal instruction
  • is not, in my experience, intrinsically hard for anyone who already has a decent grounding in some other mathematical science
  • mastering them really does mean trying to do things and failing
  • potentially hazardous. This is the idea that all that really matters is being “smart”
  • counter-productive for students to attribute their success or failure in learning about something to an innate talent

• Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley & SAS Business): Bill Franks

  Bill Franks is Chief Analytics Officer at Teradata

  tags: datascience bigdata books

  • 27. April 2012
  • 336 Seiten

• The Big Question In Big Data Is...What’s The Question? | SmartData Collective

  tags: datascience opinion bigdata

  • Some folks like to confuse Hadoop with big data
  • Focus On the Questions To Ask, Not The Answers
  • The failure of data warehouses to provide real-time data led to the creation of data marts
  • Data marts failed to provide complete and updated and comprehensive views
  • existing solutions still don’t solve the problem. Why? The market and business environment have changed
  • Data moves from structured to unstructured. Sources exponentially proliferate. Data quality is paramount.
  • Real-time is irrelevant because speed does not trump fidelity. Quantity does not trump quality
  • Business questions remained unanswered despite the massive number of reports and views and charts
  • The big shift is about moving from data to decisions

• Information Theory, Pattern Recognition, and Neural Networks

  tags: machinelearning video lectures

  • Draft videos (editing incomplete)
  • Entropy and Data Compression
  • Shannon’s Source Coding Theorem
  • Inference and Information Measures for Noisy Channels
  • Introduction to Bayesian Inference
  • Approximating Probability Distributions
  • Clustering
  • Monte Carlo Methods
  • Gibbs sampling
  • Neural Networks
  • Other course materials - free online text book [Information Theory, Inference, and Learning Algorithms]

Posted from Diigo. The rest of my favorite links are here.