You are currently browsing the monthly archive for January 2013.

  1. Machine Learning, Big Data, Deep Learning, Data Mining, Statistics, Decision & Risk Analysis, Probability, Fuzzy Logic FAQ
  2. A Funny Thing Happened on the Way to Academia . . .
  3. Advice for students on the academic job market (2013 edition)
  4. Perspective: “Why C++ Is Not ‘Back’”
  5. Is Fourier analysis a special case of representation theory or an analogue?
  6. The Beauty of Bioconductor
  7. The State of Statistics in Julia
  8. Open Source Misfeasance
  9. Book review: The Signal and The Noise
  10. Should the Cox Proportional Hazards model get the Nobel Prize in Medicine?
  11. The most influential data scientists on Twitter
  12. Here is an interesting review of Nate Silver’s book. The interesting thing about the review is that it doesn’t criticize the statistical content, but criticizes the belief that people only use data analysis for good. This is an interesting theme we’ve seen before. Gelman also reviews the review.—–Simply Statistics
  13. Video : “Matrices and their singular values” (1976)
  14. Beyond Computation: The P vs NP Problem – Michael Sipser—-This talk is arguably the very best introduction to computational complexity .
  15. What are some of your personal guidelines for writing good, clear code?
  16. How do you explain Machine learning and Data Mining to non CS people?
  17. Suggested New Year’s resolution: start a blog:  A blog forces you to articulate your thoughts rather than having vague feelings about issues; You also get much more comfortable with writing, because you’re doing it rather than thinking about doing it; If other people read your blog you get to hear what they think too. You learn a lot that way. || Set aside time for your blog every day. Keep notes for yourself on bloggy subjects (write a one-line gmail to yourself with the subject “blog ideas”).
  18. The most influential data scientists on Twitter
  19. Tips on job market interviews
  20. The age of the essay

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