Buch, Englisch, 874 Seiten, Format (B × H): 155 mm x 235 mm
ISBN: 978-981-9223-00-8
Verlag: Springer
This open access textbook provides a rigorously structured, method-oriented guide to mastering core machine learning methods essential for understanding and applying modern artificial intelligence (AI) technologies. In an era where AI is transforming every industry, (Second Edition) systematically presents the most foundational and widely used techniques across four key branches: supervised learning, unsupervised learning, deep learning, and reinforcement learning.
The book is clearly organized around algorithmic methods—such as GBDT, the EM algorithm, Transformer models, diffusion models, and PPO—that have remained central to machine learning despite rapid advancements in the field. Through concise mathematical formulations, intuitive explanations, and practical examples, it offers deep insights into over 40 essential techniques. Each volume provides a focused overview, followed by chapters that explain one or two key methods, making the content accessible for both comprehensive study and targeted reference.
Designed for advanced undergraduate and graduate students, educators, and AI professionals, this textbook serves both as a learning resource and a long-term reference. It assumes foundational knowledge in calculus, linear algebra, probability, and computer science, and supports readers in developing a structured understanding of machine learning that is both theoretical and application-oriented. Whether exploring why Transformers have revolutionized natural language processing or how PPO optimizes decision-making in reinforcement learning, this book is intended to both inform and inspire further exploration.
Zielgruppe
Upper undergraduate
Fachgebiete
Weitere Infos & Material
Volume I Supervised Learning.- Chapter 1 Introduction to Machine Learning.- Chapter 2 Introduction to Supervised Learning.- Chapter 3 Linear Regression.- Chapter 4 Perceptron.- Chapter 5 -Nearest Neighbors.- Chapter 6 The Naïve Bayes Method.- Chapter 7 Decision Trees.- Chapter 8 Logistic Regression and Maximum Entropy Models.- Chapter 9 Support Vector Machines.- Chapter 10 Boosting.- Chapter 11 Hidden Markov Models.- Chapter 12 Conditional Random Fields.- Chapter 13 Summary of Supervised Learning Methods.- Volume II Unsupervised Learning.- Chapter 14 Introduction to Unsupervised Learning.- Chapter 15 Clustering Methods.- Chapter 16 Singular Value Decomposition.- Chapter 17 Principal Component Analysis.- Chapter 18 EM Algorithm and Variational EM Algorithm.- Chapter 19 Markov Chain Monte Carlo Methods.- Chapter 20 Latent Semantic Analysis and Non-negative Matrix Factorization.- Chapter 21 Probabilistic Latent Semantic Analysis.- Chapter 22 Latent Dirichlet Allocation.- Chapter 23 Summary of Unsupervised Learning Methods.- Volume III Deep Learning.- Chapter 24 Introduction to Deep Learning.- Chapter 25 Feedforward Neural Networks.- Chapter 26 Convolutional Neural Networks.- Chapter 27 Recurrent Neural Networks.- Chapter 28 Transformer.- Chapter 29 GPT and BERT.- Chapter 30 Variational Autoencoder.- Chapter 31 Generative Adversarial Networks.- Chapter 32 Diffusion Models.- Chapter 33 Summary of Deep Learning Methods.- Volume IV Reinforcement Learning.- Chapter 34 Introduction to Reinforcement Learning.- Chapter 35 Markov Decision Processes.- Chapter 36 Multi-Armed Bandits.- Chapter 37 Value-Based Methods.- Chapter 38 Deep Q-Networks.- Chapter 39 Policy-Based Methods.- Chapter 40 Proximal Policy Optimization (PPO).- Chapter 41 Summary of Reinforcement Learning Methods.
