Numerical Machine LearningSharif JEMAA
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| Title: | Numerical Machine LearningSharif JEMAA |
|---|---|
| Description: | Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering.Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features- Provides a concise introduction to numerical concepts in machine learning in simple terms- Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables- Focuses on numerical examples while using small datasets for easy learning- Includes simple Python codes- Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses. |
| Authors: | Zhiyuan, Wang, Sayed, Ameenuddin Irfan, Christopher, Teoh, Priyanka, Hriday Bhoyar |
| Resource Type: | eBook. |
| Subjects: | Numerical analysis, Machine learning |
| Categories: | COMPUTERS / Data Science / Machine Learning, COMPUTERS / Mathematical & Statistical Software |
| Database: | eBook Collection (EBSCOhost) |
| Abstract: | Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering.Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features- Provides a concise introduction to numerical concepts in machine learning in simple terms- Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables- Focuses on numerical examples while using small datasets for easy learning- Includes simple Python codes- Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses. |
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| ISBN: | 9789815136999 9789815136982 |
