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The Paper Trail

Hi all! I'm a huge bibliophile and quite avid about education. In the context of my work in data science as a machine learning engineer, I've had to learn a lot of material extremely quickly. Every new model build brings with it a new learning curve. Below I've listed by favorite online tutorials, books, and research papers for learning about various interesting topics.


Topic Book Title Main Author Reading Level Link
Bayesian Statistics Doing Bayesian Data Analysis 1 Kruschke Introductory
Bayesian Statistics Probability Theory Jaynes Practitioner
Bayesian Statistics Bayesian Data Analysis Gelman Practitioner
Bayesian Statistics Gaussian Processes for Machine Learning Rasmussen Expert
Bayesian Statistics Statistical Decision Theory Berger Expert
Statistics Statistical Analysis Kachigan Introductory
Statistics Causality Pearl Expert
Machine Learning Introduction to Statistical Learning - Introductory
Machine Learning Elements of Statistical Learning - Practitioner
Machine Learning Pattern Recognition and Machine Learning Bishop Practitioner
Neural Networks Deep Learning Goodfellow Practitioner
Time Series Nonlinear Time Series Fan Practitioner
Time Series Introduction to Time Series Brockwell Introductory
Graphs Networks Newman Practitioner
Graphs Networks, Crowds, and Markets Kleinberg Expert
Evolutionary Algorithms Essentials of Metaheuristics Luke Practitioner

1: Python version of examples: Doing Bayesian Data Analysis

Books Runners Up

Books that are great, but which I don't consider 'must reads' either because 1) there is a better book in the above list 2) the topic covered is extremely niche or 3) the material is sufficiently advanced that only a masochist would subject their brain to learning the material.

Topic Book Title Main Author Reading Level Link
Bayesian Statistics Statistical Rethinking McElreath Introductory
Bayesian Statistics A Student's Guide to Bayesian Statistics Lambert Introductory
Bayesian Statistics Bayesian Probability Theory: Applications in the Physical Sciences Linden Practitioner
Bayesian Statistics Information Theory, Inference, and Learning Algorithms MacKay Practitioner LINK
Statistics A Modern Introduction to Probability and Statistics Dekking Introductory
Machine Learning Advanced Data Analysis from an Elementary Point of View Shalizi Expert
Cyber Security Network Security and Cryptology Musa Introductory
Cyber Security Counter Hack Reloaded Skoudis Introductory
Graphs Network Science Barabasi Introductory
Neural Networks Neural Networks and Deep Learning Nielsen Introductory
Natural Language Processing Neural Network Methods for Natural Language Processing Goldberg Practitioner
Quality Control Introduction to Statistical Quality Control Montgomery Introductory
Filtering Bayesian Filtering and Smoothing Sarkka Practitioner
Bayesian Statistics Statistics for Spatio-Temporal Data Cressie Expert
Bayesian Statistics Geostatistics: Modeling Spatial Uncertainty Chiles Expert
Time Series Elements of Forecasting Diebold Introductory
Time Series Time Series Analysis Hamilton Introductory
Time Series Introduction to Time Series Analysis and Forecasting Montgomery Introductory


Topic Course Title
Reinforcement Learning Berkeley Deep Reinforcement Leaning
Reinforcement Learning University College London Reinforcement Learning
Signal Filtering Filtering in Python
Machine Learning Lazy Programmer Inc.
Statistics Statistics and Probability
Statistics MIT Introduction to Probability and Statistics
Linear Algebra MIT Linear Algebra Sprint 2010
Machine Learning Stanford CS229
Statistics Seeing Theory
Data Visualization Fundamentals of Data Visualization


Tutorials and Examples


Projects and Datasets

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