Hi all,

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I’m a self-taught ML practitioner, working in the industry. However, I feel that my lack of formal education is hurting me, especially when working with research folks (stats & math heavy topics).

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I’m quite good at self-learning, so I’d like to revisit all the foundations in the next 12 months. I’m looking to have solid, comprehensive grasp of the most important topics in ML, so that I can at least understand conversations around me.

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Would appreciate suggestions on how improve the reading list I cooked up (what to add / remove / replace).

Do you think 12 months is a reasonable timeline for the following?

Basics

Calculus

1. Thomas’ Calculus
2. The matrix Calculus you need for deep learning

Linear Algebra

1. No bullshit guide to linear algebra

2. Linear Algebra Done Right

Probability

1. Introduction to Probability

2. MIT RES.6-012 Introduction to Probability, Spring 2018

Information Theory

1. Information Theory: A Tutorial Introduction

2. Information Theory, Inference, and Learning Algorithms

Statistics

1. All of Statistics

2. Casella, G. and Berger, R.L. (2001). “Statistical Inference”

Bayesian Statistics

1. Bayesian Data Analysis

Optimization

1. Introduction to Linear Optimization

2. Convex Optimization

ML

Foundations

1. A Course in Machine Learning

2. Machine Learning: a Probabilistic Perspective

Specifics

Causality

1. The Book of Why: The New Science of Cause and Effect

2. Causality: Models, Reasoning and Inference

DL

1. Deep Learning

2. EPFL Course

Gaussian Processes

1. Gaussian Processes for Machine Learning

NLP

1. Eisenstein’s Notes

2. A Primer on Neural Network Models for Natural Language Processing

Reinforecement Learning

1. Reinforcement Learning: An Introduction

Graphical Models

1. Probabilistic Graphical Models

Recommender Systems

1. Recommender Systems

Probabilistic Programming

1. PPLs