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14.9 MB
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8B350DBAC40F902D19CAA104C43E9226CBDB6959
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April 20, 2026, 2:53 p.m.
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(Last updated: April 20, 2026, 2:57 p.m.)
| File | Size |
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| Faul A. A Concise Introduction to Machine Learning 2ed 2025.pdf | 14.9 MB |
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SOURCE: Faul A. A Concise Introduction to Machine Learning 2ed 2025
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Textbook in PDF format A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of Machine Learning concepts from basic principles and illustrates every concept using examples in both Python and MatLAB, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content. The emphasis of the book is on the question of Why—only if “why” an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise. This useful reference should be essential on the bookshelf of anyone employing Machine Learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable. Machine Learning is known under many other names such as Artificial Intelligence (AI), Pattern Recognition, Data Mining, Data Assimilation, and Big Data, to list but a few. It developed in many areas of science, for example physics, engineering, computer science, and mathematics in parallel and independently. Example usages are spam filtering, Optical Character Recognition (OCR), search engines, computer vision, Natural Language Processing (NLP), advertising, fraud detection, robotics, data prediction, material and drug discovery, and astronomy. This makes it sometimes difficult to find a solution for a particular problem in the literature, simply because of the fact that different words and phrases are used for the same concept. This book aims to alleviate this. A common concept, but known in several disciplines under different names, is described using mathematics as the common language. Readers will find the index useful to find a particular topic as it is known to them. The index is comprehensive, making it easy to find the required information. Hopefully, this will prove the book useful as a reference and make it an essential on the bookshelves of anybody employing Machine Learning techniques
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