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Probabilistic Graphical Models Specialization By Stanford University

A rigorous and comprehensive specialization that equips learners with the theoretical and practical skills to master probabilistic graphical models.

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

What you will learn in Probabilistic Graphical Models Specialization By Stanford University Course

  • Understand the foundational concepts of probabilistic graphical models (PGMs), including Bayesian networks and Markov networks.

  • Perform exact and approximate inference in PGMs using algorithms like variable elimination, belief propagation, and Markov Chain Monte Carlo (MCMC) methods.

  • Learn parameter estimation and structure learning for both directed and undirected graphical models.

  • Apply PGMs to real-world problems in areas such as medical diagnosis, image understanding, and natural language processing.

Program Overview

 Probabilistic Graphical Models 1: Representation

⏱️ 66 hours

  • Explore the two basic PGM representations: Bayesian Networks (directed graphs) and Markov Networks (undirected graphs).

  • Understand the theoretical properties and practical uses of these representations.

  • Engage in hands-on assignments to represent real-world problems.


 Probabilistic Graphical Models 2: Inference

⏱️ 38 hours

  • Learn how PGMs can be used to answer probabilistic queries.
  • Study both exact and approximate inference algorithms, including variable elimination and belief propagation.
  • Implement key routines of inference algorithms in programming assignments.


 Probabilistic Graphical Models 3: Learning

⏱️ 66 hours

  • Delve into learning PGMs from data, focusing on parameter estimation and structure learning.
  • Understand the Expectation-Maximization (EM) algorithm and its applications.
  • Apply learning algorithms to real-world datasets in programming assignments.

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Job Outlook

  • Proficiency in PGMs is valuable for roles such as Machine Learning Engineer, Data Scientist, and AI Researcher.
  • Skills acquired in this specialization are applicable across various industries, including healthcare, finance, and technology.
  • Completing this specialization can enhance your qualifications for positions that require expertise in probabilistic modeling and machine learning.
9.5Expert Score
Highly Recommended
The "Probabilistic Graphical Models Specialization" offers a rigorous and comprehensive exploration of PGMs, balancing theoretical foundations with practical applications. It's particularly beneficial for individuals seeking to deepen their understanding of probabilistic models in complex domains.
Value
9
Price
8.9
Skills
9.4
Information
9.5
PROS
  • Taught by renowned expert Daphne Koller from Stanford University.
  • Comprehensive coverage of PGMs, from representation to learning.
  • Hands-on assignments to solidify learning.
  • Applicable to both academic research and industry applications.​
CONS
  • Requires a strong background in probability, statistics, and linear algebra.
  • Some learners may find the mathematical rigor challenging.​

Specification: Probabilistic Graphical Models Specialization By Stanford University

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

Probabilistic Graphical Models Specialization By Stanford University
Probabilistic Graphical Models Specialization By Stanford University
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