- The course is based on a reading material set that is mainly composed of research papers and book chapters.
- Each paper/chapter is assigned to a session. All the students are supposed to read it. The presenter is randomly chosen.
| Topic||Resources|| Due date|
|Deep learning|| Deep Machine Learning – A New Frontier in Artificial Intelligence Research (P1)|| 06/02/2012|
|DBN|| Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. In Proceedings of the Twenty-Sixth International Conference on Machine Learning, 2009 (slides:pdf) (slides:ppt) (video) (paper)|| 20/03/2012|
|Deep learning|| Yoshua Bengio. 2009. Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2, 1 (January 2009), 1-127. DOI=10.1561/2200000006 http://dx.doi.org/10.1561/2200000006 (chapters 1,2,3) (video) (slides:pdf)|| 27/03/2012 |
|Deep learning ||Yoshua Bengio. 2009. Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2, 1 (January 2009), 1-127. DOI=10.1561/2200000006 http://dx.doi.org/10.1561/2200000006 (chapters 4,5,6) || 10/04/2012|
| || Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19, 153. MIT;.[presentation] [pdf] - [hinton-presentation] [video-lecture]|| |
| Matrix Completion|| E. Candès and B. Recht, “Exact matrix completion via convex optimization,” Communications of the ACM, vol. 55, no. 6, p. 111, Jun. 2012. [pdf] [video] [presentation]|| |
| Sparse Coding||[video] || |