Read more
This book offers a clear and intuitive approach to the world of random signals and processes, relating them with fundamental statistics and machine learning techniques all through the lens of MATLAB. It is specifically designed for readers who prefer clarity over complexity, demystifying key concepts in random signals and processes, statistics, and machine learning. Even with a minimal amount of mathematical background, readers can confidently engage with the book, as it focuses on concepts and related terms clearly and simply.
- Provides a highly accessible introduction to the concepts and key terms related to probability, random variables and processes, statistics, and machine learning;
- Assumes minimal mathematical background and provides straightforward explanations of key terms, making the learning process comfortable and manageable;
- Introduction to core statistical concepts, descriptive and inferential statistics;
- A clear and intuitive approach to regression techniques such as linear, polynomial, and multiple regression;
- Primer on machine learning techniques, explaining their crucial role and their relationship with probability, random signals, and statistics;
- An intuitive and simplified approach to machine learning techniques such as logistic regression, naive Bayes, Gaussian naive Bayes, and KNN is provided;
- Includes many examples in MATLAB, a variety of exercises, as well as end-of-chapter quizzes for self-assessment, ensuring an interactive and engaging learning experience
List of contents
Introduction to Sample Space and Probability.- Random Variable.- Multidimensional Random Variables.- Normal Random Variable.- Other Important Random Variables.- Random Processes.- Spectral Characteristics of Random Processes.
About the author
Gordana Jovanovic Dolecek received a BSc and Ph.D. degree from the University of Sarajevo, Bosnia and Herzegovina, and an MSc degree from the University of Belgrade, Serbia. In 1995, she joined the Institute INAOE, Department for Electronics, Puebla, Mexico, where she works as a full professor. She was a visiting researcher with UCSB, Santa Barbara, USA, in 2001-2002 and 2006, with SDSU, San Diego, USA, in 2008-2009, and with UCLA, Los Angeles, USA, in 2015-2016.
She is the author/coauthor of more than 90 journals and 500 conference papers. She was the Associate Editor for IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Circuits and Systems-II: Express Briefs, and IEEE Circuits and Systems Magazine. Actually, she is the Associate Editor for IET Signal Processing, and the Senior Editor for IEEE Transactions on Circuits and Systems-II: Express Brief.
She was a committee member for more than 80 international conferences. She is a member of the CAS Digital Signal Processing (DSP) committee and Review committee (RC) DSP member for the IEEE ISCAS conferences. She was a Guest Editor for various journals, co-organizer of special sessions at international conferences, and gave many conferences and tutorials across the world.
Her research interests include Digital Signal Processing (DSP), Digital Communications, Education Methods for DSP and Digital Communications, Machine Learning (ML), and Deep Learning (DL) for DSP and Digital Communications.
She is a member of the Mexican Academy of Sciences, SNI of Mexico, and a Life Senior Member, IEEE.
In 2024, she received the IEEE Circuits and Systems Society John Choma Education Award and the Best Associate Editor IEEE TCAS II award for 2023. Additionally, she received the Best Associate Editor IEEE TCAS I Award for 2022 and the 2020-2021 period. In 2022, she was the recipient of the IEEE Puebla award. In 2012, she received the Science and Technology Puebla State award for her research work in electronics.
Summary
This book offers a clear and intuitive approach to the world of random signals and processes, relating them with fundamental statistics and machine learning techniques—all through the lens of MATLAB. It is specifically designed for readers who prefer clarity over complexity, demystifying key concepts in random signals and processes, statistics, and machine learning. Even with a minimal amount of mathematical background, readers can confidently engage with the book, as it focuses on concepts and related terms clearly and simply.
- Provides a highly accessible introduction to the concepts and key terms related to probability, random variables and processes, statistics, and machine learning;
- Assumes minimal mathematical background and provides straightforward explanations of key terms, making the learning process comfortable and manageable;
- Introduction to core statistical concepts, descriptive and inferential statistics;
- A clear and intuitive approach to regression techniques such as linear, polynomial, and multiple regression;
- Primer on machine learning techniques, explaining their crucial role and their relationship with probability, random signals, and statistics;
- An intuitive and simplified approach to machine learning techniques such as logistic regression, naive Bayes, Gaussian naive Bayes, and KNN is provided;
- Includes many examples in MATLAB, a variety of exercises, as well as end-of-chapter quizzes for self-assessment, ensuring an interactive and engaging learning experience.
