Enroll Course: https://www.coursera.org/learn/probabilistic-graphical-models-3-learning
Probabilistic Graphical Models (PGMs) are a powerful and versatile framework for modeling complex probabilistic systems, and the ‘Probabilistic Graphical Models 3: Learning’ course on Coursera offers an in-depth exploration of this fascinating topic. This course is ideal for data scientists, machine learning enthusiasts, and researchers looking to deepen their understanding of how to learn and infer with PGMs.
The course begins with a comprehensive overview of various learning tasks associated with PGMs, emphasizing the importance of these models at the intersection of statistics and computer science. It revisits fundamental machine learning concepts from Andrew Ng’s renowned course, ensuring that learners have a solid foundation before diving into more advanced topics.
One of the core sections of this course focuses on parameter estimation in Bayesian networks. It discusses maximum likelihood estimation, its limitations, and how Bayesian estimation can provide better robustness. For those interested in undirected models, the course explores learning in Markov networks, addressing the computational challenges posed by the global partition function.
Additionally, the course covers structure learning—how to determine the optimal network structure from data—discussing various scoring methods and optimization techniques. It also introduces the complexities of learning with incomplete data, presenting the Expectation-Maximization (EM) algorithm as a solution.
The course wraps up with a summary of key learning issues and a final assessment, along with an overview of real-world tradeoffs and practical considerations when deploying PGMs.
Overall, this course is highly recommended for those seeking to master the methodologies behind PGMs and their applications. Its thorough content, combined with practical insights, makes it an excellent investment for advancing your machine learning skills.
Tags: ProbabilisticGraphicalModels, MachineLearning, BayesianNetworks, MarkovNetworks, StructureLearning, ParameterEstimation, IncompleteData, EMAlgorithm, DataScience, AI
Enroll Course: https://www.coursera.org/learn/probabilistic-graphical-models-3-learning