Get updates on Eligibility, Admission, Placements Fees Structure
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 |
The Probabilistic Graphical Models 1: Representation course has the following options:
Particulars | Amount |
Course Fee, 1 Month | Rs. 1,676 |
Course Fee, 3 Month | Rs. 5,029 |
Course Fee, 6 Month | Rs. 25,146 |
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
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
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
- Distributions
- Factors
Assignment
- 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
Assignments
- Bayesian Network Fundamentals
- Bayesian Network Independencies
- Octave/Matlab installation
Programming Assignment
- Simple BN Knowledge Engineering
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
Videos
- Overview of Template Models
- Temporal Models - DBNs
- Temporal Models - HMMs
- Plate Models
Assignments
- Template Models
Videos
- Overview: Structured CPDs
- Tree-Structured CPDs
- Independence of Causal Influence
- Continuous Variables
Assignments
- Structured CPDs
- BNs for Genetic Inheritance PA Quiz
Programming Assignment
- BNs for Genetic Inheritance
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
Assignments
- Markov Networks
- Independencies Revisited
Programming Assignment
- Markov Networks for OCR
Videos
- Maximum Expected Utility
- Utility Functions
- Value of Perfect Information
Assignments
- Decision Theory
- Decision-Making PA Quiz
Programming Assignment
- Decision Making
Video
- Knowledge Engineering
Assignment
- Representation Final Exam