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A 25-minute review (in Thai) on the book

Machine Learning Refined: Foundations, Algorithms, and Applications by Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos (Northwestern University)

Book resource: https://github.com/jermwatt/machine_l...

Conclusion: recommended for senior undergrad or graduate students who study machine learning and would like to focus more on mathematical optimization underlying those models. The machine learning methods are split in linear and nonlinear approaches (both supervised and unsupervised). Visualizations in the book are well-prepared and help readers to intuitively understand how the optimization is the main work force for tuning a model. Some newly metaphor concept is introduced to explain a relation between model complexity (or capacity) and optimization computation resource. Required background topics are linear algebra, vector calculus, and basic computer programming.

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