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
