Probabilistic Machine Learning: Models, Algorithms and a Programming Library

Probabilistic machine learning provides a suite of powerful tools for modeling uncertainty, performing probabilistic inference, and making predictions or decisions in uncertain environments. In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms, a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds.

Authors

• Jun Zhu Tsinghua University National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics

Keywords

• Machine Learning: Deep Learning
• Machine Learning: Learning Generative Models
• Machine Learning: Machine Learning
• Machine Learning: Probabilistic Machine Learning