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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Probabilistische graphische Modelle
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Spezialisierung Probabilistische graphische Modelle Stanford UniversityÜber diesen Kurs
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Kurs 2 von 3 in
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Ca. 38 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
Kompetenzen, die Sie erwerben
- Inference
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 3 in
Stufe „Fortgeschritten“
Ca. 38 Stunden zum Abschließen
Englisch
Untertitel: Französisch, Portugiesisch (europäisch), Russisch, Englisch, Spanisch
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Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Lehrplan - Was Sie in diesem Kurs lernen werden
25 Minuten zum Abschließen
Inference Overview
This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference).
25 Minuten zum Abschließen
2 Videos (Gesamt 25 min)
1 Stunde zum Abschließen
Variable Elimination
This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure.
1 Stunde zum Abschließen
4 Videos (Gesamt 56 min)
18 Stunden zum Abschließen
Belief Propagation Algorithms
This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.
18 Stunden zum Abschließen
9 Videos (Gesamt 150 min)
2 Stunden zum Abschließen
MAP Algorithms
This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.
2 Stunden zum Abschließen
5 Videos (Gesamt 74 min)
15 Stunden zum Abschließen
Sampling Methods
In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings.
15 Stunden zum Abschließen
5 Videos (Gesamt 100 min)
1 Stunde zum Abschließen
Inference in Temporal Models
In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks.
1 Stunde zum Abschließen
1 Video (Gesamt 20 min)
Bewertungen
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- 1 star1,26 %
Top-Bewertungen von PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
von HL18. Mai 2020
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
von RL23. Feb. 2021
Awesome class to gain better understanding of inference for graphical model
von EZ9. März 2018
Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.
von LL11. März 2017
Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.
Über den Spezialisierung Probabilistische graphische Modelle
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.
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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