Independent studies in
Advanced Machine Learning
Maestría en Ingeniería - Ingeniería de Sistemas y Computación

Contents

• Course description
• Assignments
• Resources
• Grades

Course Description

Objective

Methodology

• 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.
• Each student must propose a project related to the course content. The student must present an intermediate report and a final report.
  
• Additionally, some assignments may be given along the semester.
  

Contents

Grading

• Presentations 60%
• Project 30%
• Proposal 7%
• Intermediate report 8%
• Final report 15%
• Other 10%

References

• [Burges05] Burges, Chris, Tal Shaked, Erin Renshaw, et al. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, 89-96. Bonn, Germany: ACM. doi:10.1145/1102351.1102363. 
  
• [Cao07] Cao, Z., T. Qin, T. Y. Liu, M. F. Tsai, and H. Li. 2007. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, 129-136.
• [Chang07] Chang, E. Y., K. Zhu, H. Wang, et al. PSVM: Parallelizing Support Vector Machines on Distributed Computers. http://books.nips.cc/papers/files/nips20/NIPS2007_0435.pdf.
• [Chapelle06] Chapelle, O., B. Scholkopf, and A. Zien. 2006. Semi-supervised learning. MIT Press.
• [Chu07] Chu, C. T., S. K. Kim, Y. A. Lin, et al. 2007. Map-Reduce for Machine Learning on Multicore. In Advances in Neural Information Processing Systems: Proceedings of the 2006 Conference. http://www-cs.stanford.edu/people/ang/papers/nips06-mapreducemulticore.pdf.
• [Herbrich00] Herbrich, R., T. Graepel, and K. Obermayer. 2000. Large Margin Rank Boundaries for Ordinal Regression. In Advances in Large Margin Classifiers . http://research.microsoft.com/users/Cambridge/rherb/papers/herobergrae99.ps.gz.
• [Joachims02] Joachims, Thorsten. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 133-142. Edmonton, Alberta, Canada: ACM. doi:10.1145/775047.775067. http://www.joachims.org/publications/joachims_02c.pdf.
• [Song08] Song, Y., W. Y. Chen, H. Bai, C. J. Lin, and E. Y. Chang. 2008. Parallel Spectral Clustering.
  http://infolab.stanford.edu/~echang/ecml08-song.pdf
• [SC04] Shawe-Taylor, J. and Cristianini, N. 2004 Kernel Methods for Pattern Analysis. Cambridge University Press.

Assignments

Resources