AmazingTutorial

http://people.revoledu.com/kardi/tutorial/index.html 1. Electronics, linux, programming, embedded system, robot, RFID http://www.penguintutor.com/index.php 2. Optimization algorithms in Machine Learning http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf 3. optimization in computer vision http://visiontrain.inrialpes.fr/?page=school1 4. Optimization Guy (UW) http://pages.cs.wisc.edu/~swright/ 5. Modern Trends in Optimization ans its application (NIPS 2010) http://www.ipam.ucla.edu/programs/op2010/ 6. Bayesian and variational Bayesian: 1. Bayesian inference tutorial: http://www.miketipping.com/papers.htm 2. Variational Bayes :   http://www.variational-bayes.org/ 3. Bayesian methods and Markov random field: http://www.lx.it.pt/~mtf/FigueiredoCVPR.pdf!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 7. CVPR 2012 tutorial: http://www.cvpr2012.org/program-details/tutorials 8. Optimization lecture notes from berkeley http://paleale.eecs.berkeley.edu/~varaiya/papers_ps.dir/NOO.pdf 9. optimization course from hamilton: http://www.networkmaths.ie/courses 10. tutorial on variational method :http://cvpr.in.tum.de/tutorials 11. Markov Random Field and Stochastic image model : http://www.cis.temple.edu/~latecki/Courses/RobotFall08/Papers/MRFBauman.pdf http://signal.ee.psu.edu/mrf.pdf 12. Mathematical methods in digital image processing:http://www.helmholtz-muenchen.de/ibb/homepage/laurent.demaret/vorlesung/SS_11/MathematicalMethods_ImageProcessing_LectureNotes_SS_11.pdf 13. Paul's online Math Notes:http://tutorial.math.lamar.edu/Classes/DE/PhasePlane.aspx 14. deep learning: http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial 15. Sparse optimization tutorial http://nuit-blanche.blogspot.com/2013/09/tutorial-on-sparse-optimization-and.html 16. Multi view feature learning (2012 CVPR): http://www.cs.toronto.edu/~rfm/multiview-feature-learning-cvpr/index.html 17. Deep Learning Book: http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf