Collection of random useful links over the internet
- Homotopy Neural Theory
- Dense CRFs
- Tensorflow debugging
- Tensorflow lecture notes Stanford Tensorflow Stanford 2
- RL-book
- ReadUp
- Spectral Universality in NN
- Conformal Prediction
- Conformal & other
- Perturbations, Optimization & Stats
- Math for ML
- MathML Math & Computation
- Linear Algebra
- MathML
- StatLearning
- StatLearning
- MathML
- TCS
- Targeted MLE
- RL bootcamp 1
- RL bootcamp2
- Extreme forecasting
- Imbalance data
- Stats&EE
- Short Tutorial Information Theory
- CMU Notes
- Foundations ML
- Evolution Stragies
- Scipy tutorial
- Stochastic Convex Optimization
- Deep Learning Resources
- Reinforcement Learning Key papers
- Reinforcement Learning Berkeley CS188 - Reinforcement Learning
- Foundations of Data Science MSR, 1, book
- The analysis of data
- Statistical Learning Theory Stanford
- AI: Principles & Techniques Stanford
- Probability Stanford
- Discrete Math Stanford
- CS Theory and Information from Geometry perspective - Foundations CMU
- Statistical Learning Theory: Illinois
- Statistical Learning Theory: UPenn
- Math for ML: MIT OCW
- Michael Jordan suggested readings
- Bandits
- Online Methods for ML
- Concentration inequalities
- 47 - 48
- Cornell notes
- Deep Learning Book
- Deep Learning Book 2
- Small Sample Theory
- Guide to convolution arithmetic
- Stanford tutorials
- Stanford tutorials 2
- Talk slides that lead to capsulenets
- Material on ML
- ML material 1
- ML material 2
- Computer Vision Book
- Probability Theory 1
- Fundamentals of computational vision
- Objective functions
- Stanford ML A.Ng
- Math book
- How to prove
- Convex optimization
- Linear Optimization
- Optimization from UPenn
- Operations Research
- Probabilistic Theory of Deep Learning
- Sparse Additive Models
- Evaluating Networks Based on Topology
- Fisher Vectors
- Half Spaces
- Bayes Opt
- Simple Theory of DL
- Utah ML material
- Geometric Optimization
- Quadratic Programming in Geometric Optimization
- Efficient Algos for Geometric Optimization
- Information Geometric Optimization
- ML Theory
- Notes on Writing Well
- Evolutionary Compuatation
- Gravity and Light Courses
- Statistical ML
- Sensitivity Analysis
- Utah notes
- OCW
- OCW Stats
- Multivariate Statistics
- Sparse Coding
- Open Stanford
- Short courses
- Advanced ML
- A Mathematician' Lament
- Advanced ML
- Linear Algebra in 4 Pages
- Global Optimization
- Online Learning
- Convex Relaxations
- Statistical Limits of Convex Relaxations
- Convex Relaxations for Permutations
- Theorem Proving
- Semidefinite Programming
- Math & Physics Lectures
- SOM 1
- SOM 2
- Scaling Bayesian Inference Thesis
- Statistical Learning Lectures
- Stochastic Convex Optimization John Duchi
- Convex Optimization 2 3 4 5
- Stats Theory
- Stats Theory 2
- ML Stanford
- Matrix Differentiation
- MaskNet
- Howgwild-Parallelizing SGD
- Exploration strategies
- Extracting comprehensible models from NN - PhD
- Deep Reinforcement Learning Berkley RL Camp Labs
- Concentration Inequalities: 1 2 3 4 5 6 7 8 9 10 11 12 13 14
- Math for ML
- Optimization lecture notes
- Ipam Workshop on GCNN video
- CVPR18-How to bee a good citizen
- Math writing 1 2 3 4 5 6
- 3 Sins of authors in cs & math
- Nice wealth of resources from MLSS.cc
- David Silver's Courlse on RL & John Schulmann's lectures
- ds3-datascience-polytechnique summer school 2018
- ML Series from Bloomberg
- Geometric Deep Learning
- Fairness in ML book
- FOPSS summer school 2018
- Turing mechanistics with generative modeling
- Shalev-Shwartz and Ben-David ML Handouts
- 2018 Summer School on “Operations Research and Machine Learning”
- Resources on books and drafts from Prof. Cosma Shalizi
- Another nice collection of lecture notes on stats, ml and optimization from Ryan Tibshirani
- Open lectures for PhD students
- Statistical relational learning summer school 2018
- 7 Tools for Causal Inference
- Deep Bayesian Summer School 2018
- CMU deep learning course 2018
- Learning from data G.Strang
- Tensorflow tutorial 2
- StarAI summer school 2018 lectures
- ILP 2018
- Non-convex Optimization NIPS 2015 Workshop
- TLA+ formal verification abstract math language
- Toronto group DNN lecture series 2014 - old but informative
- cs224 DeepNLP from Stanford
- C++ Now 2018 slides
- Probabilistic programming - MIT lectures - forestdb.org
- DeepLR Maths Winter School
- ECCV 2018 Summary
- TCS+
- Simons Institute
- BIRS videos
- Institute for Advanced Studies
- MSR talks
- Shannon Channel
- Princeton TCS videos
- Deep learning book - Sebastian Raschka
- Great series of talks around the globe on TCS+
- How to write good cvpr submission
- Theories of Deep Learning videos
- Statistical Thinking
- Adversarial Tutorial NeurIPS 2018
- Variational Bayesian Inference ICML 2018 Tutorial Tutorials NeurIPS 2018 Tutorial Slides
- Statistical Learning Theory NeurIPS 2018 Slides Additional Slides MLSS04-Taylor MLSS03-Bousquet
- AutoML book
- Reinforcement Learning Book Alt. link
- Competitive Programming
- Optimization 2017 2018
- tmux-screen cheatsheet tmux
- papers with code
- killed by google jax datasets opensource
- PHATE 1 Facets 1
- Data reproducibility DVC
- Deep learning course lectures
- Birds eye view on optimization
- Reinforcement Learning Monograph - Bertsekas
- Geometric Functional Analysis Workshop 1
- Deep Learning & Reinforcement Learning summer school 2018 Toronto
- CMU DL 2018
- Rules of ML: Best Practices for ML Engineering
- Algorithms
- Monte Carlo Approaches
- Best paper awards in CS since '96
- Topology 1
- Approximate Inference 2
- Math ref, Math proof thought process
- ADMM Examples:1, 2 - Proximal Methods Examples: 1, 2
- Metalearning
- Math review & optimization material: 1, 2, 3, 4
- Awesome Optimization Tutorial
- Introduction to Statistical Learning slides 2 ku.dk slides HKUST KOGOD Radford Neal course
- Deep Learning Bridging Theory & Practice
- Reinforcement Learning & Optimal Control
- ML Toronto
- Nice ML book
- Collection of nice courses/material from Joan Bruna
- MathStats book
- Causal analysis
- Physics & Computation
- Astrophysics and ML winter school
- Optimal Transport
- Open problems in data privacy video
- Colt open problems 2018 2019
- Future Areas in ML
- Rejections
- Lecture notes on Stats from columbia
- Theoretical DNN lectures
- Limitations of opaque learning
- Physics + ML: 1, 2
- Computational Statistics: 1, 2, 3
- AutoML book