Probabilistic Graphical Models | Carnegie Mellon University

Explore the unified framework of probabilistic graphical models and their applications in AI, statistics, computer systems, and more. Gain a solid foundation for research and problem-solving.

Introduction

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides a unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

Highlights

• Provides a unified framework for solving problems in a wide range of fields
• Enables efficient inference, decision-making, and learning in complex problems with large datasets
• Offers a strong foundation for applying graphical models and addressing research topics in the field

Recommendation

This course is recommended for graduate students or professionals interested in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology. It provides a solid foundation in probabilistic graphical models and their applications, making it a valuable resource for those looking to deepen their understanding and skills in these areas.