Probability and Statistics for Machine Learning and Data Science

This page collects my lecture notes and learning materials in probability, statistics, and applied statistical modeling for machine learning and data science.

My goal is to build a clear and structured reference that connects statistical theory with practical applications such as experimentation, regression, forecasting, and data-driven decision-making.

What this collection covers

This collection is intended to support learning in areas such as:

• probability foundations,
• statistical inference,
• regression analysis,
• experimental design,
• time series analysis,
• applied statistics for data science.

Applied statistics

Experimental design

This lecture note summarizes what I have learned about experimental design, also known as design of experiments (DOE).

Background

The note is based on the following courses:

• STAT-5303 Experimental Designs offered by Oklahoma State University
• Design of Experiments Specialization offered by Arizona State University on Coursera

Main references

• Dean, Voss, and Draguljić (2017), Design and Analysis of Experiments (2nd Edition)
• Kuehl (2000), Design of Experiments: Statistical Principles of Research Design and Analysis (2nd Edition)
• Montgomery (2017), Design and Analysis of Experiments (9th Edition)

Time series analysis

This lecture note summarizes what I have learned about time series analysis, an important area of advanced probability and statistics.

Background

The note is based on the following course:

• STAT-5053 Time Series Analysis offered by Oklahoma State University

Main references

• Cryer and Chan (2008), Time Series Analysis with Applications in R (2nd Edition)
• Montgomery, Jennings, and Kulahci (2015), Introduction to Time Series Analysis and Forecasting (2nd Edition)

Planned additions

In the future, I plan to expand this page with additional notes and projects on:

• probability theory,
• statistical inference,
• regression analysis,
• predictive modeling,
• and other topics that bridge classical statistics with modern machine learning.

Purpose of this page

This page serves as a public archive of my learning journey in probability and statistics, while also providing a structured reference for readers interested in the mathematical foundations of data science.