RNN Libraries

GroundHog
https://github.com/pascanur/GroundHog

An extension to Torch7's nn package
https://github.com/clementfarabet/lua---nnx#nnx.Recurrent

A midi learning machine.
https://github.com/blr246/midi-machine

RNNLIB
http://sourceforge.net/projects/rnnl/

CURRENNT
http://sourceforge.net/projects/currennt/

BLOCKS
http://blocks.readthedocs.org/en/latest/

MLSS 2014 Pitsburg Videolectures

Schedule and Content

• Introduction to Machine Learning
• Collaborative Filtering
• Recommender Systems
• Spectral and Tensor Methods
• Deep Learning
• Parameter Server  
• NELL
• Data Stream Analytics
• Scaling Machine Learning
• Resource-aware distributed Machine Learning 

VideoLectures on Youtube

Legendre Transform, conjugate functions and Quasi-(Concavity/Convexity)

These are some helpful links to better understand conjugate functions:

http://maze5.net/?page_id=733
https://en.wikipedia.org/wiki/Legendre_transformation
http://www.onmyphd.com/?p=legendre.fenchel.transform
http://physics.stackexchange.com/questions/4384/physical-meaning-of-legendre-transformation
http://jmanton.wordpress.com/2010/11/21/introduction-to-the-legendre-transform/

Quasiconcavity and Quasiconvexity
http://www.economics.utoronto.ca/osborne/MathTutorial/DFI.HTM#s:levelcurves

Erik Sudderth's Courses

Introduction to Machine Learning
http://cs.brown.edu/courses/csci1950-f/spring2012/calendar.html
http://cs.brown.edu/courses/csci1420/calendar.html

Probabilistic Graphical Models
http://cs.brown.edu/courses/csci2950-p/lectures.html

Learning & Inference in Probabilistic Graphical Models
http://cs.brown.edu/courses/csci2950-p/spring2010/lectures.html

Applied Bayesian Nonparametrics
http://cs.brown.edu/courses/csci2950-p/fall2011/lectures.html

David Blei's trio

COS513: Foundations of Probabilistic Modeling
http://www.cs.princeton.edu/courses/archive/fall10/cos513/

COS597C: Advanced Methods in Probabilistic Modeling

http://www.cs.princeton.edu/courses/archive/fall11/cos597C/

COS597C: Bayesian Nonparametrics
http://www.cs.princeton.edu/courses/archive/fall07/cos597C/syllabus.html

Visual Recognition and Search Topics in Information Processing

http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

Selected links for probabilistic models

NPBAYES resourses by peterorbanz
http://stat.columbia.edu/~porbanz/talks/npb-tutorial.html

Glossary by tom minka
http://alumni.media.mit.edu/~tpminka/statlearn/glossary/

PGM by murphy
http://www.cs.ubc.ca/~murphyk/Teaching/Stat521A-Spring09/index.html

Probabilistic graphical models - advanced methods by murphy
https://sites.google.com/site/cs228tspring2012/

STA561: Probabilistic Machine Learning: Fall 2013
http://genome.duke.edu/labs/engelhardt/courses/sta561.html