Articles, Talks, and Lectures I liked

  • May 2015, Andrew Ng on Creativity and Innovation,
*-Fundamentals about Machine Learning that relates  f-Divergence , Jenssen Gap, Taylor Theorm,  Statistical information, Fall 2013

1- Where machine vision needs help from machine learning, by William Freeman

2- Differential Geometry for Computer Science, Stanford Course, Spring,  2013 

2-Spectral Methods Talks by Luca , Stanford University, Spring, 2013. 

I also attended a mini-course on spectral methods in Princeton University.

4- Start-Ups

10 Strategies for StartUp Success With Jason Nazar

Good 15-step diagram to build Start-Up 

5- Machine Learning Online Course at Stanford

6- Graphical Models by Koller Online Course at Stanford

7- A Talk in Discrete Optimization with Apps to segmentation and scene parsing.
8- A Talk in  Struct SVM.

9- A talk by Antonia Torriba in MIT for shared feature Representation to quickly learn new classes from the already learnt tasks in 2006
Refer to the presentation to get the references

10-Summer School in Cambridge for Machine Learning: It includes David Blie's talk about LDA (Important to finish asap)

The summer school on Machine learning is awosome, all videos of previous years are available.

11- I also liked the online courses offered by Stanford