Probabilistic Graphical Models | Notre Dame Spring 2018

Comprehensive introduction to probabilistic graphical models, covering theory, algorithms, and real-world applications in machine learning, computer vision, and natural language processing.

Introduction

This course provides a comprehensive introduction to probabilistic graphical models, which are a powerful framework for reasoning under uncertainty. It covers the fundamental concepts, algorithms, and applications of graphical models, including Bayesian networks, Markov random fields, and undirected models.

Highlights

• Covers the theoretical foundations and practical applications of probabilistic graphical models
• Explores a wide range of algorithms for inference and learning, including exact and approximate methods
• Discusses real-world applications in areas such as machine learning, computer vision, and natural language processing
• Includes hands-on exercises and programming assignments using Python and popular libraries like scikit-learn and TensorFlow

Recommendation

This course is highly recommended for students and professionals interested in understanding and applying probabilistic graphical models. It provides a solid foundation in the principles and techniques of this powerful modeling framework, which is widely used in various fields of computer science and data science. The course is suitable for both beginners and experienced learners looking to deepen their knowledge of probabilistic modeling and its applications.

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