With tremendous improvement in computational power and availability of rich data, almost all engineering disciplines are using data science in one way or another. This textbook present material on data science comprehensively and in a structured manner.
With tremendous improvement in computational power and availability of rich data, almost all engineering disciplines are using data science in one way or another. This textbook present material on data science comprehensively and in a structured manner.
Raghunathan Rengaswamy is the Marti Mannariah Gurunath Institute Chair Professor, Dean Global Engagement, and a core member of the Robert Bosch Center for Data Science and AI (RBC-DSAI) at IIT Madras. He is a co-Founder and Director of three IITM incubated companies. Raghu's work is in systems engineering, data science, ML and AI techniques. His work in these areas has resulted in more than 140 international journal papers, one textbook, two US patents, several conference papers, and presentations. His work has been well cited and scores of students have gone through his MOOC courses: "Data Science for Engineers" and "Python for Data Science". He has received awards for his research: Young Engineer Award for the year 2000 awarded by INAE, the Graham faculty research award at Clarkson University in 2006. He has also received teaching awards: Omega Chi Epsilon professor of the year award at Clarkson in 2003, and Dr. Y.B.G. Varma award for teaching excellence at IIT Madras in 2018. He was elected a fellow of Indian National Academy of Engineering in 2017.
Inhaltsangabe
Chapter 1. Introduction to DS, ML AI Chapter 2. DS and ML - fundamental concepts Chapter 3. Linear algebra for DS and ML Chapter 4. Optimization for DS and ML Chapter 5. Statistical foundations for DS and ML Chapter 6. Function approximation methods Chapter 7. Classification methods Chapter 8. Conclusions and future directions References Index
Chapter 1. Introduction to DS, ML AI Chapter 2. DS and ML - fundamental concepts Chapter 3. Linear algebra for DS and ML Chapter 4. Optimization for DS and ML Chapter 5. Statistical foundations for DS and ML Chapter 6. Function approximation methods Chapter 7. Classification methods Chapter 8. Conclusions and future directions References Index
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