关于此课程： 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.
制作方： Stanford University
教学方： Daphne Koller, Professor
School of Engineering
| 基本信息 | 课程 2（共 3 门，Probabilistic Graphical Models Specialization ） |
| 级别 | Advanced |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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).
2 视频
- 视频: Overview: Conditional Probability Queries
- 视频: Overview: MAP Inference
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.
4 视频
- 视频: Variable Elimination Algorithm
- 视频: Complexity of Variable Elimination
- 视频: Graph-Based Perspective on Variable Elimination
- 视频: Finding Elimination Orderings
已评分: Variable Elimination
第 2 周
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.
9 视频
- 视频: Belief Propagation Algorithm
- 视频: Properties of Cluster Graphs
- 视频: Properties of Belief Propagation
- 视频: Clique Tree Algorithm - Correctness
- 视频: Clique Tree Algorithm - Computation
- 视频: Clique Trees and Independence
- 视频: Clique Trees and VE
- 视频: BP In Practice
- 视频: Loopy BP and Message Decoding
已评分: Message Passing in Cluster Graphs
已评分: Clique Tree Algorithm
已评分: Exact Inference
第 3 周
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.
5 视频
- 视频: Max Sum Message Passing
- 视频: Finding a MAP Assignment
- 视频: Tractable MAP Problems
- 视频: Dual Decomposition - Intuition
- 视频: Dual Decomposition - Algorithm
已评分: MAP Message Passing
第 4 周
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.
5 视频
- 视频: Simple Sampling
- 视频: Markov Chain Monte Carlo
- 视频: Using a Markov Chain
- 视频: Gibbs Sampling
- 视频: Metropolis Hastings Algorithm
已评分: Sampling Methods
已评分: Sampling Methods
已评分: Sampling Methods PA Quiz
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.
第 5 周
Inference Summary
This module summarizes some of the topics that we covered in this course and discusses tradeoffs between different algorithms. It also includes the course final exam.
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制作方
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.
价格
| 购买课程 | |
|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
Really a interesting, challenging and great course!
Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.
good way to learn PGM,
JL
I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!