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Course material

The unique aspect of this class is that each student is free to dive into the depth of machine learning she or he desires. Our primary goal is to make sure that the 2 hours you spend in the class are useful for you and you learn something new. So if that is the only time you can commit its fine. If you want though to get more feel free to investigate the following resources. As the class progresses we will add links to lab session next to each paper/book.


Relevant online courses


  • O'REILLY has an excellent online course for learning R essentials very quickly
  • The classic coursera machine learning intro
  • Computing for Data from coursera again is a course very similar to this one for learning R in practice
  • This one is almost the same as the previous one.




General Reading


  • An excellent guide about coding in R and also about machine learning packages that will use in the class (including RHadoop) is this book


    It is a great companion, good to have. For more details look at amazon.

  • This is also a great book for R



  • If you are beginner in machine learning and you are mostly a practitioner this is great book



    For more details take a look at amazon.

  • If you want a deeper understanding along with the maths, I recommend this book



    You can buy it from amazon or download it for free

  • A more recent an more complete book is this one



    For more details look at amazon.

  • A more concise foundation on probabilities and statistics with a good tutorial in R is the first of volume of the upcoming trilogy "the analysis of data"



    For more details you take a look at amazon. You can also download for free the R programming tutorials from the author's website.



Scientific Papers



Tutorials/Presentations



Table of Contents

Page title Most recent update Last edited by
ICML 2013 Review August 2, 2013 4:15 PM nikolaos v.
Lesson 8 April 10, 2013 1:57 PM nikolaos v.
Lesson 7 April 3, 2013 11:44 AM nikolaos v.
Other clustering December 6, 2012 3:33 PM nikolaos v.
Distributed k-means December 5, 2012 11:23 PM nikolaos v.
Introduction to k-means December 5, 2012 11:09 PM nikolaos v.
Download a virtual machine November 28, 2012 9:48 AM nikolaos v.
Lesson 3 December 6, 2012 4:21 PM nikolaos v.
Decision Tree November 16, 2012 3:21 PM nikolaos v.
Regression Tree November 16, 2012 3:10 PM nikolaos v.
Lesson 2 Run a big logistic regression November 16, 2012 2:33 PM nikolaos v.
Lesson 2 Logistic Regression November 16, 2012 2:23 PM nikolaos v.

Our Sponsors

  • Ismion Inc

    The instructor for teaching the courses

  • LogicBlox Inc

    LogicBlox offers space, equipment and instructors payment

  • Predictix

    Paying for cloud time and for TAs

  • Kabbage

    Space and great pizza

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