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.
This course is part of the Probabilistic Graphical Models Specialization
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 3 of 3 in the
Advanced Level
Approx. 66 hours to complete
English
Skills you will gain
- Algorithms
- Expectation–Maximization (EM) Algorithm
- Graphical Model
- Markov Random Field
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 3 of 3 in the
Advanced Level
Approx. 66 hours to complete
English
Offered by
Syllabus - What you will learn from this course
16 minutes to complete
Learning: Overview
16 minutes to complete
1 video (Total 16 min)
1 hour to complete
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
1 hour to complete
6 videos (Total 59 min)
2 hours to complete
Parameter Estimation in Bayesian Networks
2 hours to complete
5 videos (Total 77 min)
21 hours to complete
Learning Undirected Models
21 hours to complete
3 videos (Total 52 min)
18 hours to complete
Learning BN Structure
18 hours to complete
7 videos (Total 106 min)
22 hours to complete
Learning BNs with Incomplete Data
22 hours to complete
5 videos (Total 83 min)
Reviews
- 5 stars71.38%
- 4 stars19.52%
- 3 stars5.38%
- 2 stars3.03%
- 1 star0.67%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
by USSep 5, 2020
Amazing! This is the first specialization that I have finished and it feels amazing! Daphne was amazing!
by RCMay 6, 2020
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
by RLMar 22, 2021
Excellent course. Assignments are challenging but once you figure them out you will have a solid understanding of PGM.
by SJApr 19, 2017
Tougher course than the 2 preceding ones, but definitely worthwhile.
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