James Montgomery

Engineer, Entrepreneur, Innovator
Welcome to my blog where I share the latest in my work and interests.
If you like what you see here, please visit the main page of my website to learn more about me!

My Website

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.


Here are a few of my favorite text books for learning about topics in data science.

Topic Book Title Main Author Reading Level Source
Bayesian Statistics Doing Bayesian Data Analysis1 Kruschke Introductory LINK
Bayesian Statistics Probability Theory Jaynes Pratitioner LINK
Bayesian Statistics Bayesian Data Analysis Gelman Pratitioner LINK
Bayesian Statistics Gaussian Processes for Machine Learning Rasmussen Expert LINK
Bayesian Statistics Statistical Decision Theory Berger Expert LINK
Statistics Statistical Analysis Kachigan Introductory LINK
Statistics Causality Pearl Expert LINK
Machine Learning Introduction to Statistical Learning - Introductory LINK
Machine Learning Elements of Statistical Learning - Practitioner LINK
Machine Learning Pattern Recognition and Machine Learning Bishop Practitioner LINK
Neural Networks Deep Learning Goodfellow Practitioner LINK
Time Series Nonlinear Time Series Fan Practitioner LINK
Time Series Introduction to Time Series Brockwell Introductory LINK
Graphs Networks Newman Practitioner LINK
Graphs Networks, Crowds, and Markets Kleinberg Expert LINK
Evolutionary Algorithms Essentials of Metaheuristics Luke Practitioner LINK
Probabalistic Graphical Networks An Introduction to Probabilistic Graphical Models Michael Jordan Practitioner LINK
Probabalistic Graphical Networks Machine Learning: A Probabilistic Perspective Kevin Murphy Practitioner LINK
Probabalistic Graphical Networks Probabilistic Graphical Models Daphne Koller Practitioner LINK

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 Source
Bayesian Statistics Probabilistic Programming and Bayesian Methods for Hackers Cam Davidson-Pilon Introductory LINK
Bayesian Statistics Statistical Rethinking McElreath Introductory LINK
Bayesian Statistics A Student's Guide to Bayesian Statistics Lambert Introductory LINK
Bayesian Statistics Bayesian Probability Theory: Applications in the Physical Sciences Linden Practitioner LINK
Bayesian Statistics Information Theory, Inference, and Learning Algorithms MacKay Practitioner LINK
Statistics A Modern Introduction to Probability and Statistics Dekking Introductory LINK
Machine Learning Advanced Data Analysis from an Elementary Point of View Shalizi Expert LINK
Cyber Security Network Security and Cryptology Musa Introductory LINK
Cyber Security Counter Hack Reloaded Skoudis Introductory LINK
Graphs Network Science Barabasi Introductory LINK
Neural Networks Neural Networks and Deep Learning Nielsen Introductory LINK
Natural Language Processing Neural Network Methods for Natural Language Processing Goldberg Practitioner LINK
Quality Control Introduction to Statistical Quality Control Montgomery Introductory LINK
Filtering Bayesian Filtering and Smoothing Sarkka Practitioner LINK
Bayesian Statistics Statistics for Spatio-Temporal Data Cressie Expert LINK
Bayesian Statistics Geostatistics: Modeling Spatial Uncertainty Chiles Expert LINK
Time Series Elements of Forecasting Diebold Introductory LINK
Time Series Time Series Analysis Hamilton Introductory LINK
Time Series Introduction to Time Series Analysis and Forecasting Montgomery Introductory LINK
Reinforcement Learning Deep Reinforcment Learning Hands-On Lapan Pratitioner LINK
Bayesian Statistics The Bayesian Choice Roberts Practitioner LINK
Statistics Monte Carlo Strategies in Scientific Computing Liu Practitioner LINK
Reinforcment Learning Reinforcment Learning An Introduction Sutton Practitioner LINK


If text books aren't your thing, here are a few awesome online courses.

Topic Course Title
Reinforcement Learning Berkeley Deep Reinforcement Learning
Reinforcement Learning University College London Reinforcement Learning
Signal Processing 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


A big part of my professional development is education, both teaching and learning. I'm a firm believer that we should constantly strive to further our education throughout our lives. I've always enjoyed taking classes and reading books on a diverse range of subjects from glass blowing to the study of the Greek classics. Here I've listed a few blogs I've found to be extremely influencial on my education in datascience. Make sure to check out my own personal blog too!

Radford Neal's Blog
Eric Jang's Blog
While My MCMC Gently Samples
The Morning Paper
Lillian Weng
Darren Wilkinson's Blog
District Data Labs
Hacker News
Jason Wittenbach's Github
Mack Sweeney's Github
Josh Touyz' Blog
Keegan Hines' Blog


I also love listening to podcasts on my commute to work or on long hikes. Here are a few of my favorite technical podcasts.

Security Now
Command Line Heroes (Redhat)

Contact Me

Email: jamesoneillmontgomery@gmail.com
Linkedin: James Montgomery