Probabilistic Graphical Models 1: Representation
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Course Summary
This course covers probabilistic graphical models, which are powerful tools for representing complex probability distributions over many variables. It is ideal for students who want to learn about machine learning and artificial intelligence.Key Learning Points
- Learn how to represent and reason about uncertainty using graphical models
- Understand the principles of probabilistic inference
- Apply graphical models to real-world problems
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of probabilistic graphical models
- Be able to apply graphical models to real-world problems
- Learn how to reason about uncertainty using probabilistic inference
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of probability theory
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments
- Quizzes
Similar Courses
- Deep Learning
- Machine Learning
- Reinforcement Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
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.
Outline
- Introduction and Overview
- Welcome!
- Overview and Motivation
- Distributions
- Factors
- Basic Definitions
- Bayesian Network (Directed Models)
- 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
- Structured CPDs for Bayesian Networks
- 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
Summary of User Reviews
Read reviews for Probabilistic Graphical Models course on Coursera. Discover what users think about this course and learn about its key aspects.Key Aspect Users Liked About This Course
The course is well-structured and provides a comprehensive understanding of probabilistic graphical models.Pros from User Reviews
- Excellent course material with in-depth coverage of probabilistic graphical models
- Great lectures and clear explanations from the instructor
- Challenging assignments that help reinforce learning
- Good mix of theory and practical applications
Cons from User Reviews
- Some assignments may be too difficult for beginners
- The pace may be too fast for some learners
- Some users found the course lacking in visual aids
- The course can be quite time-consuming
Daphne Koller
4.6 Raiting
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