Stanford University - Probabilistic Graphical Models 1: Representation
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Probabilistic Graphical Models 1: Representation at Coursera Overview
Duration | 67 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 1: Representation at Coursera Highlights
- 20% started a new career after completing these courses.
- 18% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Probabilistic Graphical Models 1: Representation 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 first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.
Probabilistic Graphical Models 1: Representation at Coursera Curriculum
Introduction and Overview
Welcome!
Overview and Motivation
Distributions
Factors
Basic Definitions
Semantics & Factorization
Reasoning Patterns
Flow of Probabilistic Influence
Conditional Independence
Independencies in Bayesian Networks
Naive Bayes
Application - Medical Diagnosis
Knowledge Engineering Example - SAMIAM
Basic Operations
Moving Data Around
Computing On Data
Plotting Data
Control Statements: for, while, if statements
Vectorization
Working on and Submitting Programming Exercises
Setting Up Your Programming Assignment Environment
Installing Octave/MATLAB on Windows
Installing Octave/MATLAB on Mac OS X (10.10 Yosemite and 10.9 Mavericks)
Installing Octave/MATLAB on Mac OS X (10.8 Mountain Lion and Earlier)
Installing Octave/MATLAB on GNU/Linux
More Octave/MATLAB resources
Bayesian Network Fundamentals
Bayesian Network Independencies
Octave/Matlab installation
Template Models for Bayesian Networks
Overview of Template Models
Temporal Models - DBNs
Temporal Models - HMMs
Plate Models
Template Models
Overview: Structured CPDs
Tree-Structured CPDs
Independence of Causal Influence
Continuous Variables
Structured CPDs
BNs for Genetic Inheritance PA Quiz
Markov Networks (Undirected Models)
Pairwise Markov Networks
General Gibbs Distribution
Conditional Random Fields
Independencies in Markov Networks
I-maps and perfect maps
Log-Linear Models
Shared Features in Log-Linear Models
Markov Networks
Independencies Revisited
Decision Making
Maximum Expected Utility
Utility Functions
Value of Perfect Information
Decision Theory
Decision Making PA Quiz
Knowledge Engineering & Summary
Knowledge Engineering
Representation Final Exam
Probabilistic Graphical Models 1: Representation at Coursera Admission Process
Important Dates
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