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Books like Machine learning under a modern optimization lens by Dimitris Bertsimas


First publish date: 2019
Genre: Science/Biology
Authors: Dimitris Bertsimas
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Machine learning under a modern optimization lens by Dimitris Bertsimas

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Books similar to Machine learning under a modern optimization lens (7 similar books)

The Master Algorithm by 11860353|Pedro Domingos book cover

📘 The Master Algorithm
 by Pedro Domingos

Pedro Domingos unveils the quest for a universal learning algorithm that could extract any knowledge from data, exploring how such a machine—the Master Algorithm—might reshape business, science, and society.

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The Elements of Statistical Learning by 12158869|Trevor Hastie book cover

📘 The Elements of Statistical Learning
 by Trevor Hastie

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

★★★★★★★★★★ 4.3 (3 ratings)
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Deep Learning by 11775475|Ian Goodfellow book cover

📘 Deep Learning
 by Ian Goodfellow

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

★★★★★★★★★★ 3.7 (3 ratings)
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Pattern Recognition and Machine Learning by 8824929|Christopher M. Bishop book cover

📘 Pattern Recognition and Machine Learning
 by Christopher M. Bishop


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An Introduction to Statistical Learning by 7184749|Gareth James book cover

📘 An Introduction to Statistical Learning
 by Gareth James

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

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Machine Learning For Dummies by 7020357|John Paul Mueller book cover

📘 Machine Learning For Dummies
 by John Paul Mueller


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Bioinformatics by 8328148|Pierre Baldi book cover

📘 Bioinformatics
 by Pierre Baldi

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

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Some Other Similar Books

Convex Optimization by Stephen Boyd and Lieven Vandenberghe
Optimization Algorithms by Mykel J. Kochenderfer and Tim A. Kellogg
Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Optimization Methods in Machine Learning by Francis Bach

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