Linear algebra with machine learning and data
Author(s)
Bibliographic Information
Linear algebra with machine learning and data
（Textbooks in mathematics）
CRC Press, 2023
1st ed
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Note
Includes bibliographical references (p. 283-286) and index
Description and Table of Contents
Description
This textbook attempts to revolutionize the Advanced Linear Algebra course by offering the integration of data analysis through case studies.
Many schools are trying to find ways to incorporate data analysis into the undergrad math curriculum. The author presents a real alternative to standard textbooks.
The use of case studies to demonstrate how linear algebra can be used in data analysis separates this text from all others currently available from any major publisher.
Table of Contents
1 Graph Theory. 2. Stochastic Processes. 3. SVD and PCA. 4. Interpolation. 5. Optimization and Learning Techniques for Regression. 6. Decision Trees and Random Forests. 7. Random Matrices and Covariance Estimate. 8. Sample Solutions to Exercises.
by "Nielsen BookData"
