Between 2011 to 2020 I used another homepage https://sites.google.com/site/burlachenkok/.

My old webpage contains 151 random notes on various subjects which I did during my career in a Computer Science(CS) via mixing CS with another fields.

Below are some topics in which subject I have created public personal notes:

The notes are based on reading classical math books and classical papers in Computer Vision

• Notes about the book of A.N.Kolomogorov, S.V.Fomin - Introductionary Real Analysis.
• Notes about the book of Amir Beck, First-Order Methods in optimization, 2017. That notes has been shared with prof. Amir Beck.
• Notes about EE263, EE364A, and EE364B courses at Leland Stanford Jr. University from prof. Stephen P. Boyd. That notes has been shared with prof. Stephen P. Boyd.
• Overview of some papers in Deep Learning for Image tasks.

Miscellaneous

• Information about my course projects at Leland Stanford Jr. University when I was a remote student at that University for four years.
• Physics questions important for physics-based simulation and Robotics.
• My reflections on various problems of Machine Learning.
• My reflections on how AI and Machine Learning should be systematized.

Different notes and posts covered different aspects of engineering

• Usage of programming languages Python, Matlab, C++, Perl, R, JAVA, and software development frameworks as Qt.
• Using specialized software development frameworks: Google TensorFlow, PyTorch.
• GPU programming (CUDA, OpenCL, OpenGL).
• Developing at a user-space level for Linux/Posix OS and Windows Family OS.

Compressed notes about various mathematical tools

• Convex Optimization and Numerical Optimization.
• Deep understanding of various concepts from Statistics and Machine Learning: KL-divergence, Gini index, Math behind backpropagation.
• Notes about various mathematical tools, including Fourier Transform and Fourier Series.
• Classical things from Linear Dynamical Systems.
• Systematizing catalog of methods used on Control Theory in Applications.

Compressed information about several aspects of applying AI for practical tasks

• Understanding Deep Learning from scratch.
• Describing popular tricks for Deep Learning.
• Understanding Decision Trees from scratch.
• Considering all stages of applying Machine Learning in the industry.
• Different ways try to systematize AI and ML approaches their possibilities.
• Complete derivations and problem formulations of SVM, Logistics Regression.
• Jargon applied in Classification problems and in Machine Learning.
• Usage aspects of Z3 solver from Microsoft Research.