From Regression to Language Models: The Handbook of Machine Learning and Artificial Intelligence - Mathematics, Methods, Practice
English | October 10, 2026 | ASIN: B0HMKR6WYJ | 1768 pages | Epub | 28.02 MB
Machine learning has become a basic technology, from crop yield forecasts to language assistants. This handbook explains the methods of machine learning and artificial intelligence completely and according to a uniform scheme: How does a method work, including the mathematics it requires? What is it used for? What are its strengths and weaknesses, and where do they come from? When is it the right choice?The mathematics is not only derived but explained in words: every formula is motivated, every step is justified, and every result is interpreted, often with a small numerical example.Foundations: linear algebra, calculus, statistics, information and learning theory, evaluationClassical methods: regression, SVMs, decision trees, ensembles, Gaussian processes, clusteringDeep learning: CNNs, RNNs, transformers, graph neural networks, generative models, diffusionReinforcement learning, large language models, RAG, agents, and multimodal modelsTime series, recommender systems, causal learning, and symbolic AIExplainability, fairness, MLOps, law, and ethicsMore than 150 runnable Python examples with real outputs, decision guides in every chapter, and a profile of every method make the book both a textbook and a reference for study and practice.Andreas Graw works with data in agriculture. He is also the author of Die digitale Dreifaltigkeit, a history of hardware, software, and artificial intelligence (in German).
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