Reading group on Statistical Learning
(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/
(UCL) Reproducing kernel Hilbert spaces in Machine Learning http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/rkhscourse.html
(UCL) Introduction to Statistical Learning Theory https://cciliber.github.io/intro-slt/
Support Vector Machines by Ingo Steinwart and Andreas Christmann
(MIT) Statistical Learning Theory and Applications:
http://www.mit.edu/~9.520/fall17/
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
(Caltech) https://work.caltech.edu/telecourse.html and http://amlbook.com/
(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
(Mines-ParisTech) http://members.cbio.mines-paristech.fr/~jvert/
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