Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied.
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Anti-Aging Drugs: From Basic Research to Clinical Practice by Alexander M Vaiserman (2017)
Aging is a natural phenomenon that is peculiar to all living things. However, accumulating findings indicate that senescence could be postponed or prevented by certain approaches. Substantial evidence has emerged supporting the possibility of radical human health and lifespan extension, in particular through pharmacological modulation of aging.
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Pattern Classification by Duda (2012)
Unter Musterklassifikation versteht man die Zuordnung eines physikalischen Objektes zu einer von mehreren vordefinierten Kategorien. Auf dieser Grundlage können Computer Muster erkennen. Das Interesse an diesem Forschungsgebiet hat in den letzten Jahren, besonders im Zuge der Weiterentwicklung neuronaler Netze, stark zugenommen.
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Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis (2020)
This tutorial text offers a comprehensive perspective on machine learning, integrating both probabilistic and deterministic approaches, rooted in optimization techniques, alongside Bayesian inference principles utilizing a hierarchy of probabilistic models. The book systematically introduces major machine learning methods developed across various disciplines such as statistics, statistical and adaptive signal processing, and computer science.
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An Introduction to Statistical Learning: With Applications in R by Gareth James (2013)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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A brief history of artificial intelligence: What It Is, Where We Are, and Where We Are Going by Michael Wooldridge (2022)
From Oxford’s leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence. The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of.
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Optimization for Machine Learning by Suvrit Sra (2012)
<p>An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science.
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Python for Probability, Statistics, and Machine Learning by Jose Unpingco (2016)
Provides an updated explanation of simulating, conceptualizing, and visualizing random statistical processes, along with the application of machine learning methods.
The new edition aligns with Python version 3.7 and engages with key open-source Python communities and modules, highlighting the latest developments in the field.
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Introduction to artificial intelligence by W.Ertel (2020)
An excellent resource for self-study on artificial intelligence, offering a practical and application-focused approach. Each chapter includes study exercises, highlighted examples, definitions, theorems, and illustrative cartoons. The second edition has been updated with new material on deep learning.
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Deep Learning from Scratch by Seth Weidman (2019)
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience.
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