Probabilistic Machine Learning: Advanced Topics (Hardcover)

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

這本書是為研究機器學習和統計的研究人員和研究生而設計的，他們想要學習深度學習、貝葉斯推論、生成模型和不確定性下的決策。這本高級教材是《Probabilistic Machine Learning: An Introduction》的進階版本，提供研究人員和研究生詳細的機器學習前沿主題，包括深度生成建模、圖形模型、貝葉斯推論、強化學習和因果關係。本書將深度學習置於更大的統計背景中，並將基於深度學習的方法與基於概率建模和推論的方法統一起來。本書由來自Google、DeepMind、Amazon、普渡大學、紐約大學和華盛頓大學等頂尖科學家和領域專家貢獻，是理解機器學習中重要問題的必讀之書。本書涵蓋了生成高維輸出（如圖像、文本和圖形）的方法，討論了基於潛在變量模型的數據洞察方法，考慮了在不同分布下的訓練和測試，探討了如何使用概率模型和推論進行因果推斷和決策，並提供了相應的Python代碼。

Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.

Kevin P. Murphy 是一位在加州山景城的 Google 研究科學家，他的研究領域包括人工智慧、機器學習和貝葉斯建模。