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- Title Theory and Novel Applications of Machine Learning
- Author(s) Meng Joo Er and Yi Zhou
- Publisher: InTech; eBook (Creative Commons Licensed)
- License(s): Creative Commons License (CC)
- Hardcover/Paperback 376 pages
- eBook PDF Files
- Language: English
- ISBN-10: N/A
- ISBN-13: 978-953-7619-55-4
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Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed. The ability of learning is one of the central features of human intelligence, which makes it an important ingredient in both traditional Artificial Intelligence (AI) and emerging Cognitive Science.
Machine Learning (ML) draws upon ideas from a diverse set of disciplines, including AI, Probability and Statistics, Computational Complexity, Information Theory, Psychology and Neurobiology, Control Theory and Philosophy.
ML involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc. Real-world applications of ML are widespread such as Pattern Recognition, Data Mining, Gaming, Bio-science, Telecommunications, Control and Robotics applications. This books reports the latest developments and futuristic trends in ML.
About the Authors- N/A
- Machine Learning
- Artificial Intelligence
- Data Analysis and Data Mining
- Neural Networks
- Statistics, R Language and SAS Programming
- Operations Research (OR), Linear Programming, Optimization, and Approximation
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Foundations of Large Language Models (Tong Xiao, et al.)
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Linear Algebra for Computer Vision, Robotics, and Machine Learning
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Machine Learning in Production: From Models to Products
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Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.
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Multi-Agent Reinforcement Learning (Stefano V. Albrecht, et al.)
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Distributional Reinforcement Learning (Marc G. Bellemare, et al)
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Mathematical Analysis of Machine Learning Algorithms (Tong Zhang)
This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.
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