This course is part of the Probabilistic Graphical Models Specialization. Probabilistic graphical models PGMs are a rich framework for encoding probability distributions over complex domains: joint multivariate distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
Udaya Sree, 21, Salisbury, North Carolina. OMG! I am so grateful for King Essays! I was in a pinch and I knew I couldnt get my paper done in a timely fashion. I was skeptical and scared to use this site for several reasons, e.g., plagiarism, paying for it and not getting it, my credit card being compromised. BUT! I was wrong, I am so pleased with my paper and I encourage anyone if, in a pinch, King Essays will definitely come to the rescue.
Inference and Representation
STA, Probabilistic Machine Learning, Fall
Probabilistic Graphical Models. Spring A graphical model is a probabilistic model, where the conditional dependencies between the random variables is specified via a graph. Graphical models provide a flexible framework for modeling large collections of variables with complex interactions, as evidenced by their wide domain of application, including for example machine learning, computer vision, speech and computational biology. This course will provide a comprehensive survey of learning and inference methods in graphical models.
Probabilistic Graphical Models Specialization
Also Spring This course is an introduction to the fundamental principles of the science of information. These principles apply broadly to information storage, processing, and transmission on any device.
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 an unified view for this wide range of problems, enables 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.