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- Introduction to Probability for Data...
Introduction to Probability for Data Science
Stanley Chan This book is an introductory textbook in undergraduate probability. It has a mission: to spellout the motivation, intuition, and implication of the probabilistic tools that we use in science and engineering.
The reader will find in this book the foundations of probabilistic methods in the context of data science, with an emphasis on the inseparability of data (computation) and probability (theory) in our time.
This book is designed to fill in the knowledge gaps that all data science students need.
This leads to three goals for the book:
(i) Motivation: In an ocean of mathematical definitions, theorems and equations, the reader must understand why he should spend his time on this particular topic and not another?
(ii) Intuition: when making inferences, is there a geometric interpretation or physics beyond these equations?
(iii) Conclusion: After the reader has explored the topic, what new problems will he be able to solve?
Chapters 1-5 is a basic probability course is the backbone of this book.
Chapter 6 on sample statistics is suitable for students who wish to gain theoretical insights into probabilistic convergence.
Chapter 7 on regression and Chapter 8 on estimation best suit students who want to pursue machine learning and signal processing.
Chapter 9 discusses confidence intervals and hypothesis testing, which are critical to modern data analysis. Chapter 10 introduces random processes.
The approach for random processes is more tailored to information processing and communication systems, which are usually more relevant to electrical engineering students.
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