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

The links below contain the Matlab, Python and R code and a free chapter preview for each of my books that can be purchased here.

• 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.

Technical Notes

The Technical Notes folder contains a collection of Technical Notes on various subjects, e.g., AI, Navigation, Math, Machine Learning, etc.

The Complete Math Derivations and Proofs folder contains a collection of Math Derivations and Proofs on various subjects in AI and Machine Learning.

The Math References folder is a collection of Math References on various subjects in Mathematics, e.g., the Linear Algebra document has a list of commonly used equations that most folks learned in college (and likely forgot), with a section that lists common mistakes (so that you don’t repeat them)..

The Cheatsheet and Flow-Chart folders have great machine learning tools to help you decide the right algorithm for your data.

Technical Papers

The Technical Papers folder contains most of the journal papers, conference papers, and technical notes that I have published. Topics include: AI and machine learning, aircraft design, autopilot design, GPS-INS (“SatNav”), navigation system design, computational fluid dynamics (CFD), applied mathematics and probability theory.