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Collection of random useful links over the internet

  1. Homotopy Neural Theory
  2. Dense CRFs
  3. Tensorflow debugging
  4. Tensorflow lecture notes Stanford Tensorflow Stanford 2
  5. RL-book
  6. ReadUp
  7. Spectral Universality in NN
  8. Conformal Prediction
  9. Conformal & other
  10. Perturbations, Optimization & Stats
  11. Math for ML
  12. MathML Math & Computation
  13. Linear Algebra
  14. MathML
  15. StatLearning
  16. StatLearning
  17. MathML
  18. TCS
  19. Targeted MLE
  20. RL bootcamp 1
  21. RL bootcamp2
  22. Extreme forecasting
  23. Imbalance data
  24. Stats&EE
  25. Short Tutorial Information Theory
  26. CMU Notes
  27. Foundations ML
  28. Evolution Stragies
  29. Scipy tutorial
  30. Stochastic Convex Optimization
  31. Deep Learning Resources
  32. Reinforcement Learning Key papers
  33. Reinforcement Learning Berkeley CS188 - Reinforcement Learning
  34. Foundations of Data Science MSR, 1, book
  35. The analysis of data
  36. Statistical Learning Theory Stanford
  37. AI: Principles & Techniques Stanford
  38. Probability Stanford
  39. Discrete Math Stanford
  40. CS Theory and Information from Geometry perspective - Foundations CMU
  41. Statistical Learning Theory: Illinois
  42. Statistical Learning Theory: UPenn
  43. Math for ML: MIT OCW
  44. Michael Jordan suggested readings
  45. Bandits
  46. Online Methods for ML
  47. Concentration inequalities
  48. 47 - 48
  49. Cornell notes
  50. Deep Learning Book
  51. Deep Learning Book 2
  52. Small Sample Theory
  53. Guide to convolution arithmetic
  54. Stanford tutorials
  55. Stanford tutorials 2
  56. Talk slides that lead to capsulenets
  57. Material on ML
  58. ML material 1
  59. ML material 2
  60. Computer Vision Book
  61. Probability Theory 1
  62. Fundamentals of computational vision
  63. Objective functions
  64. Stanford ML A.Ng
  65. Math book
  66. How to prove
  67. Convex optimization
  68. Linear Optimization
  69. Optimization from UPenn
  70. Operations Research
  71. Probabilistic Theory of Deep Learning
  72. Sparse Additive Models
  73. Evaluating Networks Based on Topology
  74. Fisher Vectors
  75. Half Spaces
  76. Bayes Opt
  77. Simple Theory of DL
  78. Utah ML material
  79. Geometric Optimization
  80. Quadratic Programming in Geometric Optimization
  81. Efficient Algos for Geometric Optimization
  82. Information Geometric Optimization
  83. ML Theory
  84. Notes on Writing Well
  85. Evolutionary Compuatation
  86. Gravity and Light Courses
  87. Statistical ML
  88. Sensitivity Analysis
  89. Utah notes
  90. OCW
  91. OCW Stats
  92. Multivariate Statistics
  93. Sparse Coding
  94. Open Stanford
  95. Short courses
  96. Advanced ML
  97. A Mathematician' Lament
  98. Advanced ML
  99. Linear Algebra in 4 Pages
  100. Global Optimization
  101. Online Learning
  102. Convex Relaxations
  103. Statistical Limits of Convex Relaxations
  104. Convex Relaxations for Permutations
  105. Theorem Proving
  106. Semidefinite Programming
  107. Math & Physics Lectures
  108. SOM 1
  109. SOM 2
  110. Scaling Bayesian Inference Thesis
  111. Statistical Learning Lectures
  112. Stochastic Convex Optimization John Duchi
  113. Convex Optimization 2 3 4 5
  114. Stats Theory
  115. Stats Theory 2
  116. ML Stanford
  117. Matrix Differentiation
  118. MaskNet
  119. Howgwild-Parallelizing SGD
  120. Exploration strategies
  121. Extracting comprehensible models from NN - PhD
  122. Deep Reinforcement Learning Berkley RL Camp Labs
  123. Concentration Inequalities: 1 2 3 4 5 6 7 8 9 10 11 12 13 14
  124. Math for ML
  125. Optimization lecture notes
  126. Ipam Workshop on GCNN video
  127. CVPR18-How to bee a good citizen
  128. Math writing 1 2 3 4 5 6
  129. 3 Sins of authors in cs & math
  130. Nice wealth of resources from MLSS.cc
  131. David Silver's Courlse on RL & John Schulmann's lectures
  132. ds3-datascience-polytechnique summer school 2018
  133. ML Series from Bloomberg
  134. Geometric Deep Learning
  135. Fairness in ML book
  136. FOPSS summer school 2018
  137. Turing mechanistics with generative modeling
  138. Shalev-Shwartz and Ben-David ML Handouts
  139. 2018 Summer School on “Operations Research and Machine Learning”
  140. Resources on books and drafts from Prof. Cosma Shalizi
  141. Another nice collection of lecture notes on stats, ml and optimization from Ryan Tibshirani
  142. Open lectures for PhD students
  143. Statistical relational learning summer school 2018
  144. 7 Tools for Causal Inference
  145. Deep Bayesian Summer School 2018
  146. CMU deep learning course 2018
  147. Learning from data G.Strang
  148. Tensorflow tutorial 2
  149. StarAI summer school 2018 lectures
  150. ILP 2018
  151. Non-convex Optimization NIPS 2015 Workshop
  152. TLA+ formal verification abstract math language
  153. Toronto group DNN lecture series 2014 - old but informative
  154. cs224 DeepNLP from Stanford
  155. C++ Now 2018 slides
  156. Probabilistic programming - MIT lectures - forestdb.org
  157. DeepLR Maths Winter School
  158. ECCV 2018 Summary
  159. TCS+
  160. Simons Institute
  161. BIRS videos
  162. Institute for Advanced Studies
  163. MSR talks
  164. Shannon Channel
  165. Princeton TCS videos
  166. Deep learning book - Sebastian Raschka
  167. Great series of talks around the globe on TCS+
  168. How to write good cvpr submission
  169. Theories of Deep Learning videos
  170. Statistical Thinking
  171. Adversarial Tutorial NeurIPS 2018
  172. Variational Bayesian Inference ICML 2018 Tutorial Tutorials NeurIPS 2018 Tutorial Slides
  173. Statistical Learning Theory NeurIPS 2018 Slides Additional Slides MLSS04-Taylor MLSS03-Bousquet
  174. AutoML book
  175. Reinforcement Learning Book Alt. link
  176. Competitive Programming
  177. Optimization 2017 2018
  178. tmux-screen cheatsheet tmux
  179. papers with code
  180. killed by google jax datasets opensource
  181. PHATE 1 Facets 1
  182. Data reproducibility DVC
  183. Deep learning course lectures
  184. Birds eye view on optimization
  185. Reinforcement Learning Monograph - Bertsekas
  186. Geometric Functional Analysis Workshop 1
  187. Deep Learning & Reinforcement Learning summer school 2018 Toronto
  188. CMU DL 2018
  189. Rules of ML: Best Practices for ML Engineering
  190. Algorithms
  191. Monte Carlo Approaches
  192. Best paper awards in CS since '96
  193. Topology 1
  194. Approximate Inference 2
  195. Math ref, Math proof thought process
  196. ADMM Examples:1, 2 - Proximal Methods Examples: 1, 2
  197. Metalearning
  198. Math review & optimization material: 1, 2, 3, 4
  199. Awesome Optimization Tutorial
  200. Introduction to Statistical Learning slides 2 ku.dk slides HKUST KOGOD Radford Neal course
  201. Deep Learning Bridging Theory & Practice
  202. Reinforcement Learning & Optimal Control
  203. ML Toronto
  204. Nice ML book
  205. Collection of nice courses/material from Joan Bruna
  206. MathStats book
  207. Causal analysis
  208. Physics & Computation
  209. Astrophysics and ML winter school
  210. Optimal Transport
  211. Open problems in data privacy video
  212. Colt open problems 2018 2019
  213. Future Areas in ML
  214. Rejections
  215. Lecture notes on Stats from columbia
  216. Theoretical DNN lectures
  217. Limitations of opaque learning
  218. Physics + ML: 1, 2
  219. Computational Statistics: 1, 2, 3
  220. AutoML book

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