Statistical Learning Theory

Prerequisites & Overview:

• Details about Matrices - The Matrix Cookbook
• Review of Probability and Statistics by by David Blei - Prob. & Stat
• Review of Probability Theory by Arian Maleki - Prob Theory
• Propositional Logic - Prop. Logic
• Propositional Logic, CMU - Prop. Logic
• Graphical Models in a Nutshell - Graphical Model
• Introductory Machine Learning - MIT - Learning MIT
• Introduction to Statistical Learning Theory - UPF - SLT UPF
• Statistical Learning Theory: A Tutorial - SLT Tutorial
• Overview of statistical learning theory - SLT overview
• A few notes on Statistical Learning Theory - SLT notes
• Selected topics on robust statistical learning theory - Robust SLT

Courses:

• Statistical Learning Theory & Applications by Tomaso Poggio - SLT MIT
• Statistical Learning Theory by Percy Liang - SLT Stanford
• Statistical Learning Theory by Tengyu Ma - SLT Stanford
• Statistical Learning Theory by Peter Bartlett - SLT UC Berkeley
• Statistical Learning Theory by Sham Kakade - SLT UPENN
• Statistical Learning Theory by Martin Wainwright - SLT UC Berkeley
• Computational and Statistical Learning Theory and Applications by Nati Srebro - SLT UChicago
• Machine Learning by Alessandro Verri and Lorenzo Rosasco - ML MIT
• Machine Learning by Ehsan Elhamifar - ML NEU
• Machine Learning by Tina Eliassi-Rad - ML Rutgers
• Statistical Learning Theory and Applications - SLT MIT

Books:

• A github link for ML books pdf - ML books
• A Probabilistic Theory of Pattern Recognition by L. Devroye, L. Gyorfi and G. Lugosi - Patern Recognition
• Statistical Learning Theory by Vladimir N. Vapnik - SLT
• Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar - ML foundation
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman - Elements of SL 1, Elements of SL 2
• An Elementary Introduction to Statistical Learning Theory by Sanjeev Kulkarni and Gilbert Harman - SLT Introduction
• Learning Theory: An Approximation Theory Viewpoint by Felipe Cucker and Ding Xuan Zhou - SLT Ap. Theory
• An Introduction to Computational Learning Theory by Kearns and Vazirani - Computational SLT
• Combinatorial methods in density estimation by Devroye and Lugosi - Density Estimation
• Empirical Processes in M-Estimation by van de Geer - M Estimation
• Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. Bartlett - NN Learning
• Machine Learning: A Probabilistic Perspective by Kevin P. Murphy - ML
• Pattern recognition and machine learning by Christopher Bishop - PR & ML
• Probabilistic Graphical Models by Daphne Koller and Nir Friedman - Graphicals Models
• Machine Learning by Tom Mitchell - ML
• Information Theory, Inference, and Learning Algorithms by David MacKay - Inference & Learning
• The LION Way: Machine Learning plus Intelligent Optimization by Roberto Battiti and Mauro Brunato - LION
• Statistical Learning with Sparsity by Trevor Hastie, Robert Tibshirani and Martin Wainwright - Learning with Sparsity

Readings for SLT:

• Machine Learning Readings - ML reading
• Readings of Statistical Learning Theory - SLT reading
• Advanced Topics in SLT - SLT Materials
• Readings of Statistical Learning Theory - SLT reading
• Readings of Statistical Learning Theory - SLT reading

Blogs:

• Machine Learning blog - Microsoft blog
• Google AI blog - AI
• Google DeepMind - DeepMind blog
• Algorithmia blog - Algorithm