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Contents

Other Resources

Courses

1. NYU CDS: Probability and Statistics

2. Stanford Probability and Statistics

3. NYU CDS: Inference and Representation

4. NYU CDS: Big Data 2015

5. NYU CDS: Machine Learning

6. Foundations of Graphical Models by David Blei – see Basics of Graphical Models

   1. see also a video on d-separation by Pieter Abbeel

   2. semantics of graphical models (here called “Boiler plate diagrams”) and an extended visual language Directed Factor Graph Notation for Generative Models Laura Dietz, which is the basis of the tikz-bayesnet package

7. Algorithms for Convex Optimization by Nisheeth K. Vishnoi

8. Introduction to Causal Inference by Brady Neal

9. Michael Jordan’s lecture notes on notes on Probabilistic Graphical Models

10. MIT lecture notes on algorithms for inference

11. Kevin Murphy, Machine Learning: a Probabilistic Perspective (4th eddition) | online @ NYU Libraries.

12. Probabilistic Programming and Bayesian Methods for Hackers by Cam Davidson Pilon

Short courses / tutorials

1. Basic Python

2. Plotting and Programming with Python

Linear Algebra

1. Essence of linear algebra youtube videos by 3blue1brown

2. Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares, Stephen Boyd and Lieven Vandenberghe

3. Linear dynamical systems

4. Linear Algebra done right

5. NUMERICAL LINEAR ALGEBRA Lloyd N. Trefethen and David Bau, III

6. Scientific Computing for PhDs

Books

1. All of Statistics by Wasserman

2. PRML

3. Mathematics for Machine Learning

4. Elements of Causal Inference by Jonas Peters, Dominik Janzing and Bernhard Schölkopf free PDF

5. Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009

Influential texts

1. Knuth Calculus

2. Functional Differential Geometry by Gerald Jay Sussman and Jack Wisdom

Misc

1. NeurIPS astro tutorial with datasets etc.

2. Paper about statistical combinations from phys/astro authors

3. Gentle Introduction to Automatic Differentiation on Kaggle

4. Short notes on divergence measures by Danilo Rezende

5. Lecture notes on: Information-theoretic methods for high-dimensional statistics, by Yihong Wu

Meta

The 10 most helpful *free1. online machine learning courses, via @chipro

Full thread: https://t.co/RUcG2AL1uC#MondayMotivation pic.twitter.com/Fd3sN2u7UV

— MIT CSAIL (@MIT_CSAIL) August 17, 2020

Data Science, what is it? Bibliography