Probabilistic Graphical Models 3: Learning

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About this course: 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 pr…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

Created by:  Stanford University
  • Taught by:  Daphne Koller, Professor

    School of Engineering
Basic Info Course 3 of 3 in the Probabilistic Graphical Models Specialization Level Advanced Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.7 stars Average User Rating 4.7See what learners said 课程作业

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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.

Syllabus


WEEK 1


Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.


1 video expand


  1. Video: Learning: Overview


Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.


6 videos expand


  1. Video: Regularization: The Problem of Overfitting
  2. Video: Regularization: Cost Function
  3. Video: Evaluating a Hypothesis
  4. Video: Model Selection and Train Validation Test Sets
  5. Video: Diagnosing Bias vs Variance
  6. Video: Regularization and Bias Variance


Parameter Estimation in Bayesian Networks



This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.


5 videos expand


  1. Video: Maximum Likelihood Estimation
  2. Video: Maximum Likelihood Estimation for Bayesian Networks
  3. Video: Bayesian Estimation
  4. Video: Bayesian Prediction
  5. Video: Bayesian Estimation for Bayesian Networks

Graded: Learning in Parametric Models
Graded: Bayesian Priors for BNs

WEEK 2


Learning Undirected Models



In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.


3 videos expand


  1. Video: Maximum Likelihood for Log-Linear Models
  2. Video: Maximum Likelihood for Conditional Random Fields
  3. Video: MAP Estimation for MRFs and CRFs

Graded: Parameter Estimation in MNs
Graded: CRF Learning for OCR

WEEK 3


Learning BN Structure



This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.


7 videos expand


  1. Video: Structure Learning Overview
  2. Video: Likelihood Scores
  3. Video: BIC and Asymptotic Consistency
  4. Video: Bayesian Scores
  5. Video: Learning Tree Structured Networks
  6. Video: Learning General Graphs: Heuristic Search
  7. Video: Learning General Graphs: Search and Decomposability

Graded: Structure Scores
Graded: Tree Learning and Hill Climbing
Graded: Learning Tree-structured Networks

WEEK 4


Learning BNs with Incomplete Data



In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.


5 videos expand


  1. Video: Learning With Incomplete Data - Overview
  2. Video: Expectation Maximization - Intro
  3. Video: Analysis of EM Algorithm
  4. Video: EM in Practice
  5. Video: Latent Variables

Graded: Learning with Incomplete Data
Graded: Expectation Maximization
Graded: Learning with Incomplete Data

WEEK 5


Learning Summary and Final
This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.


1 video expand


  1. Video: Summary: Learning

Graded: Learning: Final Exam

PGM Wrapup
This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.


1 video expand


  1. Video: PGM Course Summary
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