Acerca de este Programa Especializado

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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.

Resultados profesionales del estudiante
50%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
20%
Conseguiste un aumento de sueldo o ascenso.
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel avanzado
Aprox. 4 meses para completar
Sugerido 11 horas/semana
Inglés (English)
Subtítulos: Inglés (English)
Resultados profesionales del estudiante
50%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
20%
Conseguiste un aumento de sueldo o ascenso.
Certificado para compartir
Obtén un certificado al finalizar
Cursos 100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel avanzado
Aprox. 4 meses para completar
Sugerido 11 horas/semana
Inglés (English)
Subtítulos: Inglés (English)

Hay 3 cursos en este Programa Especializado

Curso1

Curso 1

Probabilistic Graphical Models 1: Representation

4.7
estrellas
1,265 calificaciones
277 revisiones
Curso2

Curso 2

Probabilistic Graphical Models 2: Inference

4.6
estrellas
437 calificaciones
63 revisiones
Curso3

Curso 3

Probabilistic Graphical Models 3: Learning

4.6
estrellas
269 calificaciones
41 revisiones

ofrecido por

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Universidad de Stanford

Preguntas Frecuentes

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • The Specialization has three five-week courses, for a total of fifteen weeks.

  • This class does require some abstract thinking and mathematical skills. However, it is designed to require fairly little background, and a motivated student can pick up the background material as the concepts are introduced. We hope that, using our new learning platform, it should be possible for everyone to understand all of the core material.

    Though, you should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access (programming assignments will be conducted in Matlab or Octave). It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).

  • For best results, the courses should be taken in order.

  • No.

  • You will be able to take a complex task and understand how it can be encoded as a probabilistic graphical model. You will now know how to implement the core probabilistic inference techniques, how to select the right inference method for the task, and how to use inference to reason. You will also know how to take a data set and use it to learn a model, whether from scratch, or to refine or complete a partially specified model.

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