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* ''' All the announcements and handouts area on Piazza, follow the link below.'''
* '''The recitation times and places are on Piazza; they have changed from what is in the catalog.'''

== Useful Links ==

* [https://piazza.com/mit/fall2012/6867/home Piazza] is our course discussion system and mailing list; we will also use it for distributing handouts and announcements.  Please subscribe to 6.867 on Piazza if you haven't already, otherwise you may miss announcements. You will also miss out on all the useful discussion on the site.
* E-mail staff at [mailto:6867-staff-2012@lists.csail.mit.edu 6867-staff-2012@lists.csail.mit.edu]

'''Project'''
* Project resources [[Project]]

'''Programming Resources'''
* Installation instructions: [[MATLAB_Installation_Instructions|MATLAB]], [[Python_Installation_Instructions|Python]]
* We compiled a [[media:matlab-getstarted.pdf|Not-too-Long MATLAB Tutorial]] for MATLAB Beginners, which covers most stuff needed in this course.
* Here are some [[Numpy|analogous tutorials]] for using NumPy in Python.
* [[media:matlab-recitation.pdf|Notes of the MATLAB recitation session]] These notes outline what you need to know before writing MATLAB code, as well as the problems and solutions that were covered in the first recitation session.

'''Mathematics'''
* Matrix cookbook [https://courses.csail.mit.edu/6.867/wiki/images/7/77/Matrix_cookbook.pdf pdf]
* Lagrange multipliers [https://courses.csail.mit.edu/6.867/wiki/index.php?title=File:Lagrange.pdf]
* A Tutorial on E-M [https://courses.csail.mit.edu/6.867/wiki/images/b/b5/Em_tutorial.pdf]

''' Books'''
* These two books (taken together) provide good coverage for the course material. 
* [http://research.microsoft.com/en-us/um/people/cmbishop/prml/ Bishop, "Pattern Recognition and Machine Learning", 2007]
* [http://www-stat.stanford.edu/~tibs/ElemStatLearn/ Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2nd edition, 2009]

== Course Information ==
* This introductory course on machine learning will give an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
* Prerequisites: (6.041 or 18.05) and 18.06; 6.034 is helpful
* Lectures: Tue. and Thu. 9:30AM - 11AM in 54-100