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.
Este curso forma parte de Programa especializado: modelos gráficos de probabilidades
Acerca de este Curso
Habilidades que obtendrás
- Expectation–Maximization (EM) Algorithm
- Graphical Model
- Markov Random Field
Programa - Qué aprenderás en este curso
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
Parameter Estimation in Bayesian Networks
Learning Undirected Models
Learning BN Structure
Learning BNs with Incomplete Data
- 5 stars71,38 %
- 4 stars19,52 %
- 3 stars5,38 %
- 2 stars3,03 %
- 1 star0,67 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
Acerca de Programa especializado: modelos gráficos de probabilidades
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Learning Outcomes: By the end of this course, you will be able to
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