Probabilistic machine learning: advanced topics
Series: Adaptive Computation and Machine LearningPublication details: Cambridge, Massachusetts: The MIT Press, 2023.Description: xxxi, 1319p.: col. ill.; hbk.: 23cmISBN:- 9780262048439
- 006.31015192 MUR
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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IIT Gandhinagar | General | 006.31015192 MUR (Browse shelf(Opens below)) | 1 | Available | 033527 |
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006.31015 MOI Algorithmic aspects of machine learning | 006.310151 KNE Math for deep learning: what you need to know to understand neural networks | 006.310151 NIE Essential math for data science: take control of your data with fundamental linear algebra, probability, and statistics | 006.31015192 MUR Probabilistic machine learning: advanced topics | 006.312 AGG Machine learning for text | 006.312 AGG Frequent pattern mining | 006.312 DAM Learning spark: lightning-fast data analytics |
Includes Index and Bibliography
An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
• Covers generation of high dimensional outputs, such as images, text, and graphs
• Discusses methods for discovering insights about data, based on latent variable models
• Considers training and testing under different distributions
• Explores how to use probabilistic models and inference for causal inference and decision making
• Features online Python code accompaniment
https://mitpress.mit.edu/9780262048439/probabilistic-machine-learning/
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