Books like Introduction to Machine Learning by Ethem Alpaydin


First publish date: 2009
Genre: Technology & Computing/Artificial Intelligence
Authors: Ethem Alpaydin
★ ★ ★ ★ ★ 5.0 (1 community ratings)

Introduction to Machine Learning by Ethem Alpaydin

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Books similar to Introduction to Machine Learning (14 similar books)

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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.

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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.

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Machine learning by 4295122|Ethem Alpaydin book cover

📘 Machine learning

A clear, concise guide to the fundamentals of machine learning, the branch of artificial intelligence that designs computer programs capable of learning from data and underpinning modern technologies like recommendation systems, face‑recognition, and autonomous vehicles.

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A comprehensive guide to probability theory with applications in statistics and machine learning, suitable for graduate students and professionals alike.

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📘 An Introduction to Statistical Learning

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 by 11485779|Kevin P. Murphy book cover

📘 Machine learning

A comprehensive introduction to machine learning, combining breadth and depth, and written in an informal, accessible style with pseudo-code for algorithms.

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📘 Bioinformatics

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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Machine Learning, Revised and Updated Edition by 4295122|Ethem Alpaydin book cover

📘 Machine Learning, Revised and Updated Edition

A concise overview of how computer programs learn from data and the implications of those learnings.

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Some Other Similar Books

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, SHAI SHALVO
Machine Learning Yearning by Andrew Ng
Probabilistic Graphical Models: Principles and Techniques by Daphne Koller, Nir Friedman
Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto
Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, Mark A. Hall

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