Stanford University - Probabilistic Graphical Models 2: Inference
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Probabilistic Graphical Models 2: Inference at Coursera Overview
Duration | 38 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Probabilistic Graphical Models 2: Inference at Coursera Highlights
- 33% started a new career after completing these courses.
- 12% got a tangible career benefit from this course.
- 12% got a pay increase or promotion.
- Earn a shareable certificate upon completion.
Probabilistic Graphical Models 2: Inference at Coursera Course details
- 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 the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.
Probabilistic Graphical Models 2: Inference at Coursera Curriculum
Inference Overview
Overview: Conditional Probability Queries
Overview: MAP Inference
Variable Elimination Algorithm
Complexity of Variable Elimination
Graph-Based Perspective on Variable Elimination
Finding Elimination Orderings
Variable Elimination
Belief Propagation Algorithms
Belief Propagation Algorithm
Properties of Cluster Graphs
Properties of Belief Propagation
Clique Tree Algorithm - Correctness
Clique Tree Algorithm - Computation
Clique Trees and Independence
Clique Trees and VE
BP In Practice
Loopy BP and Message Decoding
Message Passing in Cluster Graphs
Clique Tree Algorithm
MAP Algorithms
Max Sum Message Passing
Finding a MAP Assignment
Tractable MAP Problems
Dual Decomposition - Intuition
Dual Decomposition - Algorithm
MAP Message Passing
Sampling Methods
Simple Sampling
Markov Chain Monte Carlo
Using a Markov Chain
Gibbs Sampling
Metropolis Hastings Algorithm
Sampling Methods
Sampling Methods PA Quiz
Inference in Temporal Models
Inference in Temporal Models
Inference Summary
Inference: Summary
Inference Final Exam
Probabilistic Graphical Models 2: Inference at Coursera Admission Process
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