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The significantly reorganized revised edition of the book (2023) [2] expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build ...
OpenStax textbooks follow a traditional peer review process aimed at ensuring they meet a high quality standard before publication. Textbooks are developed and peer-reviewed by educators in an attempt to ensure they are readable and accurate, meet the scope and sequence requirements of each course, are supported by instructor ancillaries, and are available with the latest technology-based ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The authors described it as consisting of "free books written by yet unpublished authors," yet this is factually incorrect. These books were published by self-published ("indie") authors who priced them at free; the books were downloaded without the consent or permission of Smashwords or Smashwords authors and in violation of the Smashwords ...
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling.The author explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments.
An open textbook is a textbook licensed under an open license, and made available online to be freely used by students, teachers and members of the public.Many open textbooks are distributed in either print, e-book, or audio formats that may be downloaded or purchased at little or no cost.
Mohri is the author of the reference book Foundations of Machine Learning [7] used as a textbook in many graduate-level machine learning courses. [8] Mohri is also a member of the Lothaire group of mathematicians with the pseudonym M. Lothaire and contributed to the book on Applied Combinatorics on Words. [9]