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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 Probabilistic Graphical Models 1: Representation course by Coursera is a part of the Probabilistic Graphical Models Specialization on the Coursera platform. Stanford University offers a certification course in association with Coursera.

The Probabilistic Graphical Models 1: Representation online course has a curriculum spread out over five weeks. The certification course will help you develop a deep understanding of the PGM framework by communicating perplex material with skill. Moreover, the online course primarily covers the fundamentals of Bayesian networks and Markov networks.

Furthermore, the Probabilistic Graphical Models 1: Representation certification course promotes interaction and improvement with a comprehensive curriculum, intermittent assignments, and discussion boards. Candidates can self-assess using these mediums and grasp the course contents at their own pace.

Finally, Coursera provides a certificate upon successful completion of the Probabilistic Graphical Models 1: Representation course. The certificate can be added to the resume or shared on LinkedIn and other professional websites. Learners can also take a print out of the certificate.

The Highlights

Shareable certificate
Deadlines are flexible
Advanced level course
Financial Aid available
Lectures in English
Subtitles available in English, French, Portuguese (Brazilian), Spanish, Russian
Fast completion (approximately 66 hours)

Programme Offerings

Certificate
Discussion boards
email updates
Shareable Certificate
Graded Programming Assignments
hands on learning

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

You can access a 3-course Specialization in Probabilistic Graphical Models Specialization and get a shareable certificate upon completion. Financial aid is also available to those who want to purchase the course certificate but have financial difficulties.

The Probabilistic Graphical Models 1: Representation course has the following options:

Particulars	Amount
Course Fee, 1 Month	Rs. 4,023
Course Fee, 3 Month	Rs. 8,046
Course Fee, 6 Month	Rs. 12,069

Eligibility Criteria

To avail of the certificate for the Probabilistic Graphical Models 1: Representation programme, candidates need to complete the practice exercises in the certification course.

What you will learn

Machine learning

Upon successful completion of all the modules of the Probabilistic Graphical Models 1: Representation course by Coursera, you will become adept in the following:

Knowledge of Bayesian Networks and Markov Networks which are two basic PGM representations
In-depth understanding of the representation and semantics in the Bayesian network
Learn to model a real-world problem using the Bayesian network
How to create Dynamic Bayesian networks
Gain knowledge of the decision theory framework
Use a log-linear model to represent a Markov network
Use a plate model to encode domains which require structure repeats

Who it is for

StatisticianMachine Learning Engineer

Admission Details

To apply for Probabilistic Graphical Models 1: Representation course:

Step 1. Go to the Coursera website.

Step 2. Search for ‘Probabilistic Graphical Models 1: Representation’ to open the course information page.

Step 3. Next, locate the ‘Enrol for Free’ option and click on it.

Step 4. A sign-up prompt will appear. Use your email ID or Google account to sign up for the same.

Application Details

You can enrol for the Probabilistic Graphical Models 1: Representation certification course on Coursera free of cost with your email address or Google account. Choose the type of enrolment you prefer and you will get access to course materials and certificates accordingly.

The Syllabus

Videos

Welcome!
Overview and Motivation
Factors
Distributions

Practice Exercise

Basic Definitions

Videos

Semantics and Factorization
Reasoning Patterns
The 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

Readings

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 resources10m

Practice Exercise

Bayesian Network Fundamentals
Bayesian Network Independencies
Octave/Matlab installation

Videos

Overview of Template Models
Temporal Models - DBNs
Temporal Models - HMMs
Plate Models

Practice Exercise

Template Models

Videos

Overview: Structured CPDs
Tree-Structured CPDs
Independence of Causal Influence
Continuous Variables

Practice Exercise

Structured CPDs
BNs for Genetic Inheritance PA Quiz

Videos

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

Practice Exercise

Markov Networks
Independencies Revisited

Videos

Maximum Expected Utility
Utility Functions
Value of Perfect Information

Practice Exercises

Decision Theory
Decision-Making PA Quiz

Video

Knowledge Engineering

Practice Exercise

Representation Final Exam

Instructors

Stanford Frequently Asked Questions (FAQ's)

1: What can I avail, if I subscribe for the Specialization?

Upon enrolment, you will be able to access all the courses that are a part of the Specialization. Upon successful completion of the course, you will get an electronic certificate that can be added to your Accomplishments page. You can take a print out of the certificate or add it to your LinkedIn profile.

2: Post-enrollment, from when will I get access to course material?

You will get access to the course as soon as you register on the Coursera website. However, partial or full access to the content depends on the type of subscription that you have chosen.

3: Is it possible to get financial aid?

The Probabilistic Graphical Models 1: Representation course by Coursera surely provides financial aid to individuals. You can apply for financial help on the website, and Coursera will inform you about the approval within 15 days of receiving the application.

4: Will I earn any university credit for completing the course?

The Probabilistic Graphical Models 1: Representation course does not carry university credit, but some universities accept Course Certificates for credit. You can check with your university to confirm the same and submit the course certificate if required.

5: What are the career outcomes of this course?

About 23% of course takers started new careers post-certification, and 22% received tangible benefits career-wise.