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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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.
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 AKNov 8, 2017
Awesome course... builds intuitive thinking for developing intelligent algorithms...
by LCFeb 22, 2019
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
by LLJan 29, 2018
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.
by WZMar 5, 2017
Excellent course! Everyone interested in PGM should consider!
About the Probabilistic Graphical Models Specialization

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