Stanford University - Probabilistic Graphical Models 3: Learning
- Offered byCoursera
Probabilistic Graphical Models 3: Learning at Coursera Overview
Duration | 66 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Probabilistic Graphical Models 3: Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Probabilistic Graphical Models Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 66 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Probabilistic Graphical Models 3: Learning 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 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.
Probabilistic Graphical Models 3: Learning at Coursera Curriculum
Learning: Overview
Learning: Overview
Regularization: The Problem of Overfitting
Regularization: Cost Function
Evaluating a Hypothesis
Model Selection and Train Validation Test Sets
Diagnosing Bias vs Variance
Regularization and Bias Variance
Maximum Likelihood Estimation
Maximum Likelihood Estimation for Bayesian Networks
Bayesian Estimation
Bayesian Prediction
Bayesian Estimation for Bayesian Networks
Learning in Parametric Models
Bayesian Priors for BNs
Learning Undirected Models
Maximum Likelihood for Log-Linear Models
Maximum Likelihood for Conditional Random Fields
MAP Estimation for MRFs and CRFs
Parameter Estimation in MNs
Learning BN Structure
Structure Learning Overview
Likelihood Scores
BIC and Asymptotic Consistency
Bayesian Scores
Learning Tree Structured Networks
Learning General Graphs: Heuristic Search
Learning General Graphs: Search and Decomposability
Structure Scores
Tree Learning and Hill Climbing
Learning BNs with Incomplete Data
Learning With Incomplete Data - Overview
Expectation Maximization - Intro
Analysis of EM Algorithm
EM in Practice
Latent Variables
Learning with Incomplete Data
Expectation Maximization
Learning Summary and Final
Summary: Learning
Learning: Final Exam
PGM Course Summary
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