Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

Lecture notes on Bayesian deep learning

Rust for data analysis encyclopedia (WIP).

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

考研数学同济高等数学第七版线性代数浙大概率论

A comprehensive exploration of Statistics and Probability Theory concepts, with practical implementations in Python

Unofficial solutions for Introduction to Probability, Second Edition by Joseph Blitzstein and Jessica Hwang.

Applied Probability Theory for Everyone

:rocket: A library designed to facilitate work with probability, statistics and stochastic calculus

A quick introduction to all most important concepts of Probability Theory, only freshman level of mathematics needed as prerequisite.

Chapter notes and exercise solutions for Reinforcement Learning: An Introduction by Sutton and Barto

Comprehensive resources for data science interview preparation in Russian: assignments, math problems, logic tasks, live coding examples, and leetcode breakdowns.

An open-source toolkit for entropic data analysis

A Comprehensive AX = XB Calibration Solvers in Matlab

:notebook_with_decorative_cover: This repository is for storing my Higher Mathematics learning journey

Common Code for Competitive Programming in C++

My solutions to Paul L. Meyer's "Introductory Probability and Statistical Applications, 2nd ed.", ISBN 0-201-04710-1.

A math resource for CS student (I have decided to refactor the contents to my personal blog and continue working on this, so the project is archived)

Mathematical preliminaries for machine learning

The lecture notes for my discrete mathematics classes.

Quizzes & Assignment Solutions for Data Science Math Skills on Coursera. Also included a few resources on side that I found helpful.

Probability Calculations with Random Numbers via Slot Machine Spin Simulation. Includes Probability Table and Results. Console App C#.

Interactive Mathematica notebooks illustrating the course content of VE401, Probabilistic Methods in Engineering at UM-SJTU Joint Institute.

Methods used in the paper "Plausible Uncertainties for Human Pose Regression".

Materials for Probability Theory and Modelling

A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.

Official implementation of the paper: "CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessment"

A MATLAB project which applies the central limit theorem on PDFs and CDFs of different probability distributions.

Course notes from several math and computer science courses from CUNY Queens College.

Clean Random Events for Probabilistic Reasoning in Python