Publications

I focus on clear declaration of variables and documentation of equations/algorithms, e.g., full derivations and proofs, not found elsewhere in the literature.

Book Resources

Then links below contain the Matlab and Python code and a free chapter preview for each of my books, including:

• A Comprehensive Approach to Data Science, Machine Learning and AI.
• Math Refresher for Data Science, Machine Learning and AI.
• Math Handbook for Data Science, Machine Learning and AI.
• How to do Research.
• Optimal Nonlinear Bayesian Estimation and Sensor Fusion.
• The Absolute Beginners Guide to Neural Networks.

Technical Notes

This folder is a collection of technical reference notes on various subjects, e.g., Navigation, Math, Machine Learning, etc. For example Algebra has a list of commonly used algebra equations that most folks learned in high school (and likely forgot), with a section of common algebra mistakes (so that you don’t repeat the mistakes).

Technical Papers

This folder contains most of the journal papers, conference papers, and technical notes that I have published. The topics range from computational fluid dynamics (CFD), aircraft design, autopilot design, GPS-INS (“SatNav”) and navigation system design, applied mathematics and probability theory.

ML Flow Chart

This great machine learning flow chart will help you decide the right algorithm for your data.

You can view the flow-chart here.