Description
The Probabilistic Graphical Models Specialization is a set of specialized training in probabilistic drawing. Probabilistic graphical models provide a rich framework for decoding probability distributions. The concepts in this course are actually a common statistics and data science chapter based on concepts from probability theory, graph algorithms, machine learning and more. These are the foundations of the latest technological 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
- Spread of beliefs
- Drawing models
- Markov random field
- Gibbs sampling
- Monte Carlo Markov Chain (MCMC)
- Algorithms
- Expectation – Maximization (EM) Algorithm
Course details:
Publisher: Coursera
Instructor: Daphne Koller
French language
Education level: Advanced
Number of Courses: 3
Duration: Assuming 11 hours per week, 4 months
Probabilistic Graphical Models Specialization Series Courses:
COURSE 1
Probabilistic graphical models 1: Representation
COURSE 2
Probabilistic Graphical Models 2: Inference
COURSE 3
Probabilistic graphical models 3: Learning
Prerequisites for probabilistic graphical models:
This course requires abstract thinking and mathematical skills. However, it is designed to require relatively little knowledge and a motivated student can pick up the basic material as the concepts are introduced. We hope that by using our new learning platform, it should be possible for everyone to understand all the basic material.
However, you must 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 done in Matlab or Octave). It is also helpful to have some prior exposure to the basic concepts of discrete probability theory (independence, conditional independence, and Bayes rule).
Pictures
Examples of videos Probabilistic graphical models:
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
This collection includes 3 different courses.
Download link
Probabilistic graphical models 1: Representation
Probabilistic Graphical Models 2: Inference
Probabilistic graphical models 3: Learning
File password(s): ngaur.com
Cut
In total, about 2.1 GB