James Montgomery

Engineer, Entrepreneur, Innovator
Welcome to my blog where I share the latest in my work and interests.
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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. You can also find the recommendations below, and more, in this google sheet: Paper Trail Google Sheet


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

Topic Book Title Main Author Reading Level Source
Causal Inference Causality Pearl Expert LINK
Causal Inference Counterfactuals and Causal Inference Winship Expert LINK
Causal Inference Causal Inference: What If 2 Robins Practitioner LINK
Cyber Security Network Security and Cryptology Musa Introductory LINK
Cyber Security Counter Hack Reloaded Skoudis Introductory LINK
Evolutionary Algorithms Essentials of Metaheuristics Luke Practitioner LINK
Filtering Bayesian Filtering and Smoothing Sarkka Practitioner LINK
Graphs Networks, Crowds, and Markets Kleinberg Expert LINK
Graphs Network Science Barabasi Introductory LINK
Graphs Networks Newman Practitioner LINK
Machine Learning Advanced Data Analysis from an Elementary Point of View Shalizi Expert LINK
Machine Learning Generalized Linear Models Nelder Introductory LINK
Machine Learning Foundations of Data Science Kannan Introductory LINK
Machine Learning Pattern Recognition and Machine Learning Bishop Practitioner LINK
Machine Learning Doing Analysis using Regression and Multilevel/Hieratchical Models Gelman Practitioner LINK
Machine Learning Pattern Classification Duda Practitioner LINK
Markov Chain Monte Carlo Handbook of Markov Chain Monte Carlo Gelman Introductory LINK
Markov Chain Monte Carlo Monte Carlo Statistical Methods Casella Introductory LINK
Markov Chain Monte Carlo Markov Chain Monte Carlo in Practice Gilks Practitioner LINK
Markov Chain Monte Carlo Monte Carlo Strategies in Scientific Computing Liu Practitioner LINK
Natural Language Processing Neural Network Methods for Natural Language Processing Goldberg Practitioner LINK
Neural Network Neural Networks and Deep Learning Nielsen Introductory LINK
Neural Network Deep Learning Goodfellow Practitioner LINK
Optimization Algorithms for Optimizations Kochenderfer Introductory LINK
Probabalistic Graphical Models An Introduction to Probabilistic Graphical Models Jordan Practitioner LINK
Probabalistic Graphical Models Machine Learning: A Probabilistic Perspective Murphy Practitioner LINK
Probabalistic Graphical Models Probabilistic Graphical Models Koller Practitioner LINK
Quality Control Introduction to Statistical Quality Control Montgomery Introductory LINK
Reinforcement Learning Deep Reinforcment Learning Hands-On Lapan Practitioner LINK
Reinforcement Learning Reinforcment Learning An Introduction Sutton Practitioner LINK
Statistics Statistical Analysis Kachigan Introductory LINK
Statistics A Modern Introduction to Probability and Statistics Dekking Introductory LINK
Statistics Statistical Inference Casella Introductory LINK
Statistics Handbook of Biological Statistics McDonald Introductory LINK
Statistics Mostly Harmless Economics Pischke Introductory LINK
Statistics Experimental Design Cox Introductory LINK
Statistics Sampling Techniques Cochran Introductory LINK
Statistics Field Experiments Gerber Introductory LINK
Statistics Monte Carlo Strategies in Scientific Computing Liu Practitioner LINK
Statistics Design of Observational Studies Rosenbaum Practitioner LINK
Statistics Sampling: Design and Analysis Lohr Practitioner LINK
Statistics Graphical Models, Exponential Families, and Variational Inference Jordan Practitioner LINK
Statistics Handbook of Statistical Methods NIST Practitioner LINK
Statistics (Bayesian) Gaussian Processes for Machine Learning Rasmussen Expert LINK
Statistics (Bayesian) Statistical Descision Theory Berger Expert LINK
Statistics (Bayesian) Statistics for Spatio-Temporal Data Cressie Expert LINK
Statistics (Bayesian) Geostatistics: Modeling Spatial Uncertainty Chiles Expert LINK
Statistics (Bayesian) Probabalistic Reasoning in Intelligent Systems Pearl Expert LINK
Statistics (Bayesian) Probabilistic Programming and Bayesian Methods for Hackers Davidson-Pilon Introductory LINK
Statistics (Bayesian) Statistical Rethinking McElreath Introductory LINK
Statistics (Bayesian) A Student's Guide to Bayesian Statistics Lambert Introductory LINK
Statistics (Bayesian) Think Bayes Downey Introductory LINK
Statistics (Bayesian) Theory of Probability Jeffreys Introductory LINK
Statistics (Bayesian) The Foundations of Statistics Savage Introductory LINK
Statistics (Bayesian) Doing Bayesian Data Analysis 1 Kruschke Introductory LINK
Statistics (Bayesian) Probability Theory Jaynes Practitioner LINK
Statistics (Bayesian) Bayesian Data Analysis Gelman Practitioner LINK
Statistics (Bayesian) Bayesian Probability Theory: Applications in the Physical Sciences Linden Practitioner LINK
Statistics (Bayesian) Information Theory, Inference, and Learning Algorithms MacKay Practitioner LINK
Statistics (Bayesian) The Bayesian Choice Roberts Practitioner LINK
Statistics (Bayesian) Bayesian Artificial Intelligence Korb Practitioner LINK
Statistics (Bayesian) Bayesian Nonparametrics Ghosh Practitioner LINK
Statistics (Bayesian) Bayesian Nonparametrics Muller Practitioner LINK
Statistics (Bayesian) Modeling and Reasoning with Bayesian Networks Darwiche Practitioner LINK
Statistics (Bayesian) Bayesian Learning for Neural Networks Radford Practitioner LINK
Statistics (Bayesian) Bayesian Forcasting and Dynamic Models Harrison Practitioner LINK
Time Series Introduction to Time Series Brockwell Introductory 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
Time Series Nonlinear Time Series Fan Practitioner LINK
Time Series Timeseries Hamilton Practitioner LINK

1. Python version of examples: Doing Bayesian Data Analysis
2. Python version of examples: Causal Inference: What If


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
Statistics StatLec
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)
Lex Friedman AI Podcast

Contact Me

Email: jamesoneillmontgomery@gmail.com
Linkedin: James Montgomery