Reading group on Statistical Learning

1. (Stanford) Elements of Statistical Learning by Trevor Hastie and Rob Tibshirani,  http://statweb.stanford.edu/~tibs/ElemStatLearn/ and  http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ 

2. (UCL) Reproducing kernel Hilbert spaces in Machine Learning http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/rkhscourse.html 

3. (UCL) Introduction to Statistical Learning Theory  https://cciliber.github.io/intro-slt/

4. Support Vector Machines by Ingo Steinwart and Andreas Christmann

5. (MIT) Statistical Learning Theory and Applications:

   • http://www.mit.edu/~9.520/fall17/ 

     • http://www.mit.edu/~9.520/fall15/ 

     • http://lcsl.mit.edu/courses/ml/1617/MLNotes.pdf (L. Rosasco, Introductory Machine Learning Notes)

     • http://www.mit.edu/~9.520/fall17/Classes/mathcamp.html  (Functional Analysis and Probability Basics)

     • https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O 

     • https://www.youtube.com/playlist?list=PLyGKBDfnk-iCXhuP9W-BQ9q2RkEIA5I5f

     • http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-statistical-learning-theory-and-applications-spring-2006/

     • http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-statistical-learning-theory-and-applications-spring-2003/ 

6. (Caltech) https://work.caltech.edu/telecourse.html and http://amlbook.com/

7. (Fields Institute) http://fields2015bigdata2inference.weebly.com/materials.html  

   • Graduate Course on Topics in Inference for Big Data http://www.fields.utoronto.ca/video-archive/event/1284 

   • Graduate Course on Large Scale Machine Learning http://www.fields.utoronto.ca/video-archive/event/1283

   • Statistical Inference, Learning and Models in Big Data https://arxiv.org/abs/1509.02900 

8. (Mines-ParisTech) http://members.cbio.mines-paristech.fr/~jvert/ 

   • • http://members.cbio.mines-paristech.fr/~jvert/teaching/ 

     • http://videolectures.net/jean_philippe_vert/

     • http://members.cbio.mines-paristech.fr/~jvert/talks/

9. (ENS) http://www.di.ens.fr/%7Efbach/ 

   • • http://www.di.ens.fr/%7Efbach/#courses

     • http://www.di.ens.fr/%7Efbach/#tutorials  

General References

• "What is Machine Learning, and where is it headed?"  http://videolectures.net/mlas06_mitchell_itm/

• "The Discipline of Machine Learning" by Tom M. Mitchell, http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf

• "What is data mining ?" by Mauro Maggioni, cf. http://www.ams.org/notices/201204/rtx120400532p.pdf

• "50 years of Data Science" by David Donoho, cf. http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf

• Support Vector Machines with Applications http://www.stat.washington.edu/courses/stat527/s14/readings/MoguerzaMunoz_2006.pdf

• Introduction to Statistical Learning Theory   http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf2819.pdf

Other references

• • • Entropy, Compactness and the Approximation of Operators (Cambridge Tracts in Mathematics) by Bernd Carl, Irmtraud Stephani

    • Gaussian Hilbert Spaces (Cambridge Tracts in Mathematics) by Svante Janson

• Gaussian Measures (Mathematical Surveys and Monographs) by Vladimir I. Bogachev

• Kernel-Based Methods for Hypothesis Testing: A Unified View by Zaid Harchaoui, Francis Bach, Olivier Cappé, Eric Moulines - https://hal.inria.fr/hal-00841978 

• Introduction to Hilbert Spaces with Applications by Lokenath Debnath, Piotr Mikusinski

Coding

• https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-6

• Python Machine Learning by Sebastian Raschka, cf. http://sebastianraschka.com/books.html

• R Cookbook by Paul Teetor

• https://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-0

Bibliography

• http://www.stat.washington.edu/courses/stat527/s14/readings.shtml

• http://www.stat.washington.edu/courses/stat527/s14/readings/