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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Coursera Probabilistic Graphical Models 3: Learning certification course covers probabilistic graphical models, commonly known as PGMs. They are a rich framework used for encoding probability distributions over complex domains. They form the foundation of state-of-the-art methods in different applications such as speech recognition, natural language processing, medical diagnosis, image understanding, and many more. They are also instrumental in formulating many machine learning problems.

The Probabilistic Graphical Models 3: Learning training is the third course in a series of three. Where the first course focused on representation, and the second focused on inference and the final course helps in addressing questions related to learning. The course discusses the main problems of parameter estimation in both directed and undirected models.

Moreover, students can receive a course completion certificate for the Coursera Probabilistic Graphical Models 3: Learning programme. Candidates can attach the certificate to their LinkedIn profile or their resume/CV.

The Highlights

Advanced-level course
Flexible deadlines
Online training
Course delivery in English with multilingual subtitles
Self-paced education
A Stanford University offering
Approximately 66 hours to complete
Financial aid available
Practice quizzes
Shareable certificate
Free enrolment

Programme Offerings

Online Course
financial aid
Shareable Certificate
Flexible Deadlines
video transcript
Practise Exercises
self-directed training
Pre-recorded Video Lectures
66 Hours For Completion

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

Candidates can join the Probabilistic Graphical Models 3: Learning certification fee is free of cost by clicking on the “Enroll for Free” button. However, they have to make the fee payment, if they wish to get access to the graded course items and the course completion certificate. Also, students will be given a seven-day free trial option.

Probabilistic Graphical Models 3: Learning Fee Structure:

Subscription	Amount
1 Month	₹4,018
3 Month	₹12,056
6 Month	₹24,113

Eligibility Criteria

To get the Probabilistic Graphical Models 3: Learning certification by Coursera, candidates must watch all the video lectures, read the study material and also complete all the practise exercises. Candidates also need to get the minimum passing marks. On fulfilling all the specifications, candidates will get a course completion certificate from Coursera, which can be attached to their LinkedIn page or to their resume/CV.

What you will learn

Knowledge of Algorithms

By the end of the Probabilistic Graphical Models 3: Learning certification syllabus, candidates should be able to:

Implement MAP parameter estimation and maximum likelihood for Markov networks
Implement both Bayesian parameter estimation and maximum likelihood for Bayesian networks
Implement the Expectation-Maximisation (EM) algorithm for a Bayesian network
For a given situation, evaluate which scoring function is appropriate
Formulate a structure learning problem as a combinatorial optimisation task, over network structure
Utilise PGM inference algorithms so as to support more effective parameter estimation for PGMs

Who it is for

Machine Learning Engineer

The people who want to be ML Engineers can without any doubt pursue this Coursera programme:

Admission Details

Candidates planning to pursue Coursera Probabilistic Graphical Models 3: Learning classes online programme can follow the steps given below to apply:

Go to https://www.coursera.org/.
In Coursera’s programme catalogue, search for “Probabilistic Graphical Models 3: Learning” and open the course information page.
Choose the “Enroll for Free” option.
Existing account holders can log in and enrol for the course. New users need to create an account or use Google, Facebook, or Apple ID to log in.

Application Details

Candidates are not required to fill out a separate application form to join the Probabilistic Graphical Models 3: Learning programme by Coursera. Sign up with Coursera using their Google, Facebook, or Apple account to get access to the learning material for free. Besides, the students who desire a certificate for the course can purchase the certification experience.

The Syllabus

Video

Overview

Video

Diagnosing Bias vs Variance
Regularisation: Cost Function
Evaluating a Hypothesis
Regularisation: The Problem of Overfitting
Model Selection and Train Validation Test Sets
Regularisation and Bias Variance

Videos

Maximum Likelihood Estimation
Bayesian Prediction
Maximum Likelihood Estimation for Bayesian Networks
Bayesian Estimation
Bayesian Estimation for Bayesian Networks

Practice Exercises

Learning in Parametric Models
Bayesian Priors for BNs

Videos

Maximum Likelihood for Log-Linear Models
MAP Estimation for MRFs and CRFs
Maximum Likelihood for Conditional Random Fields

Practice Exercise

Parameter Estimation in MNs

Videos

Likelihood Scores
Structure Learning Overview
Learning General Graphs: Search and Decomposability
Bayesian Scores
BIC and Asymptotic Consistency
Learning Tree Structured Networks
Learning General Graphs: Heuristic Search

Practice Exercises

Structure Scores
Tree Learning and Hill Climbing

Videos

Overview
Analysis of EM Algorithm
EM in Practice
Expectation-Maximisation - Introduction
Latent Variables

Practice exercises

Learning with Incomplete Data
Expectation Maximization

Video

Summary: Learning

Practice exercise

Learning: Final Exam

Video

Course Summary

Instructors

Stanford Frequently Asked Questions (FAQ's)

1: What skills will I acquire after completing this course?

Upon completion of the Coursera Probabilistic Graphical Models 3: Learning programme, candidates will learn the computation of statistics from a dataset, implementation of the Expectation-Maximisation algorithm, and Bayesian parameter estimation, along with MAP parameter estimation, and much more.

2: Which University is offering the Probabilistic Graphical Models 3: Learning course?

The Probabilistic Graphical Models 3: Learning online programme is the third and final course in the Probabilistic Graphical Models Specialisation programme, offered by Stanford University.

3: How soon will I get the Probabilistic Graphical Models 3: Learning online course completion certificate?

As the certificate is a paid option, candidates need to request the completion certificate. The certificate can be requested before, during, or after completing the course.

4: How much time do I need to complete the Probabilistic Graphical Models 3: Learning course?

The Probabilistic Graphical Models 3: Learning course can be completed in approximately 66 hours.

5: Can I get access to practice exercises?

Yes, students get access to suggested readings, graded quizzes, and practice assignments after completing every module.

6: Are there any university credits for completing the course?

The Probabilistic Graphical Models 3: Learning course does not carry any university credit.