[Coursera] Probabilistic Graphical Models Specialization


[Coursera] Probabilistic Graphical Models Specialization


Probabilistic Graphical Models Specialization is a set of specialized probabilistic drawing training courses. Probabilistic graphical models are a rich framework for decoding probability distributions. The concepts of this course are in fact a common chapter of statistics and data science based on concepts from probability theory, graph algorithms, machine learning and more. These are the foundations of the latest technology methods, which include a variety of applications, including medical diagnostics, image perception, speech recognition, natural language processing, and more.

Skills you will learn in the Probabilistic Graphical Models Specialization set:

  • Inference
  • Bayesian Network
  • Belief Propagation
  • Drawing models
  • Markov Random Field
  • Gibbs sampling
  • Markov Chain Monte Carlo (MCMC)
  • Algorithms
  • Expectation – Maximization (EM) Algorithm

Course details:

Publisher: Coursera
Instructor: Daphne Koller
Language: English
Education Level: Advanced
Number of Courses: 3
Duration: Assuming 11 hours per week, 4 months

Courses in the Probabilistic Graphical Models Specialization series:

Probabilistic Graphical Models 1: Representation

Probabilistic Graphical Models 2: Inference

Probabilistic Graphical Models 3: Learning

Prerequisites for Probabilistic Graphical Models:

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).

Size: 1.85 GB

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