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Noticias de la compañía :
- Probabilistic Machine Learning: An Introduction - pml-book
'Probabilistic Machine Learning: An Introduction' is the most comprehensive and accessible book on modern machine learning by a large margin It now also covers the latest developments in deep learning and causal discovery
- Probabilistic Machine Learning: Advanced Topics - pml-book
It provides an in-depth coverage of a wide range of topics in probabilistic machine learning, from inference methods to generative models and decision making It gives a modern perspective on these topics, bringing them up to date with recent advances in deep learning and representation learning
- “Probabilistic machine learning”: a book series by Kevin Murphy
Book 1: “Probabilistic Machine Learning: An Introduction” (2022) See this link Book 2: “Probabilistic Machine Learning: Advanced Topics” (2023)
- Preface - pml-book
In 2012, I published a 1200-page book called Machine Learning: A Probabilistic Perspective, which provided a fairly comprehensive coverage of the field of machine learning (ML) at that time, under the unifying lens of probabilistic modeling
- Contents
1 1 What is machine learning? 1 1 2 Supervised learning 1 1 2 1 Classification 2 1 2 2 Regression 8 1 2 3 Overfitting and generalization 12 1 2 4 No free lunch theorem 13 1 3 Unsupervised learning 14 1 3 1 Clustering 14 1 3 2 Discovering latent “factors of variation” 15 1 3 3 Self-supervised learning 16 1 3 4 Evaluating unsupervised
- Machine Learning: a Probabilistic Perspective - pml-book
"This book covers an impressive range of the state-of-the-art in statistical machine learning It defines a clear and broadly accessible path that begins with the fundamentals of probability, and leads to a rich toolbox of statistical models and learning algorithms " -- Prof Erik Sudderth, Brown University
- Teaching material for - pml-book
Teaching material for Probabilistic Machine Learning: An Introduction Solutions to selected exercises (Official instructors can contact MIT Press for full solution manual )
- Bibliography — State Space Models: A Modern Approach - pml-book
Probabilistic Numerics: Computation as Machine Learning 2023 URL: https: www probabilistic-numerics org textbooks KGomezO12 Hilbert J Kappen, Vicenç Gómez, and Manfred Opper Optimal control as a graphical model inference problem Mach Learn , 87(2):159–182, May 2012 URL: https: doi org 10 1007 s10994-012-5278-7 Mur23 K P Murphy
- pyprobml notebooks. md at auto_notebooks_md · probml pyprobml · GitHub
pyprobml notebooks md at auto_notebooks_md · probml pyprobml · GitHub
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