

Introduction To Machine Learning (Smola & Vishwanathan) - helwr
http://alex.smola.org/drafts/thebook.pdf

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helwr
note that it's a draft and some pages are missing.

There is a course by one of the authors with good lecture notes:
<http://www.stat.purdue.edu/~vishy/introml/introml.html>

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helwr
Also note that this book is not for the faint of heart.

For introductory material you may want to take a look at some of these
courses:

Shalizi, 35-350: <http://www.stat.cmu.edu/~cshalizi/350/>

Shalizi, 36-402: <http://www.stat.cmu.edu/~cshalizi/402/>

Ng, CS229: <http://www.stanford.edu/class/cs229/materials.html>

Ng, CS294: <http://www.stanford.edu/class/cs294a/>

Roth, CS446: <http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-10/lectures.html>

Jebara, COMS4771: <http://www.cs.columbia.edu/~jebara/4771/handouts.html>

Jebara, COMS6772: <http://www.cs.columbia.edu/~jebara/6772/solutions.html>

Jordan, CS294: <http://www.cs.berkeley.edu/~jordan/courses/294-fall09/>

Kumar, EECS6898: <http://www.sanjivk.com/EECS6898/lectures.html>

Collins, COMS6998:
[http://www.cs.columbia.edu/~mcollins/courses/e6998-3/index.h...](http://www.cs.columbia.edu/~mcollins/courses/e6998-3/index.html)

Jaakkola & Collins, 6.867: <http://courses.csail.mit.edu/6.867/lectures.html>

Hamilton, CS831: <http://www2.cs.uregina.ca/~hamilton/courses/831/>

Girolami, MLM:
[http://www.dcs.gla.ac.uk/~girolami/Machine_Learning_Module_2...](http://www.dcs.gla.ac.uk/~girolami/Machine_Learning_Module_2006/)

Bengio, IC-49: <http://bengio.abracadoudou.com/lectures/>

Lin, CMSC838:
[http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/syllab...](http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/syllabus.html)

~~~
damienfir
Thank you very much for these resources.

