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Quick Facts

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

Course Overview

This certification course in Bayesian Statistics: Mixture Models by Coursera is a one stop place to learn the Bayesian Statistics and its Mixture Models along with its applications in the practical world. The course introduces one of the useful classes of statistics. This course is structured in a way to teach the most practical uses of Bayesian Statistics and its tools. It encourages candidates taking this course to practice and learn as the course progresses by not simply watching the videos but also constantly solving the Bayesian Statistics problems.

The course also utilises the industry software R in some places for solving the course problems. R is an easily available free statistical software that is used in m multiple industries of development, design and manufacturing, etc. The course lays out a basic tutorial to learn the software and teach the uses of it in Bayesian Statistics. Candidates are also encouraged to learn further about R for better advantages in their career.

The course is an intermediate level certificate course in Bayesian Statistics: Mixture Models taught by some of the expert faculty of the University of California Santa Cruz. For candidates who wish to take this course, it should be noted that prior knowledge of principles of estimation in maximum likelihood, Calculus based probability, and the Bayesian estimation is required. The course is a great way to take a step further in the statistics learning field and can provide you with the required edge in your career and the industry.

The Highlights

Online course
Offered by the University of California Santa Cruz
A shareable certificate
Approx 21 hours to complete
Advanced level
Flexible deadlines
Quizzes, video lectures, discussion prompts, and peer-reviewed assignments

Programme Offerings

Peer reviewed assignments
Short quizzes
Graded Assignments
video lectures
discussions
Background reading

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	Coursera

The Certification Course On Bayesian Statistics: Mixture Models is available for purchase at the price of Rs. 6,486
This fee includes certificate charges. The certificate will be given only after the candidates complete the full course and submit the graded assignments.
The course can also be taken on a free trial or in the “audit the course” mode.

Duration	Fees in INR
1 month	Rs. 6,486
3 months	Rs. 12,973
6 months	Rs. 19,459

Eligibility Criteria

Certification Qualifying Details

Candidates who complete all the video lectures and submit the graded assignments etc will be awarded a certificate in Bayesian Statistics: Mixture Models after the completion of the course.
Certificates can be shared directly on their LinkedIn profiles and attached to candidate portfolios and CV, etc.

What you will learn

Statistical skills

By taking this certification programme in Bayesian Statistics: Mixture Models candidates will learn the following:

Bayesian Statistics and its related Mixture models along with their applications and uses in the industry for making better informed decisions.
Increasing the probability of favourable outcomes in any given task by analysing and making the use of probability techniques taught in the course.
Use of mixture models in clustering and density estimation.
Hands-on experience of working with industry best practices and tools in the statistics field for better implementation and analysis of work with the practical examples taught in the class.
Practical learning and takeaways from all the short quizzes, peer reviewed assignments and discussions that impart an understanding of working of Bayesian Statistics and its relevant applications.

Who it is for

Data AnalystStatistician

Admission Details

For admission into the Bayesian Statistics: Mixture Models Course candidates have to follow the process mentioned below:

Step 1: Visit the Course page. https://www.coursera.org/learn/mixture-models

Step 2: If you have a Coursera account simple login and move to the next step. For the first time users, Coursera will direct you to sign up, candidates can also use a Google account or a Facebook account to log in to Coursera.

Step 3: Candidates can read the details of course here and move to fee payment. If you wish to take the course for free choose the ‘free audit’ option. This mode is great for learning but does not allow a certificate on completion.

Step 4: Candidates can also take a free 7-day trial. This is a nice way to experience Coursera Plus before buying it.

Step 5: Candidates who want to purchase the course need to make the monthly fee payment.

Step 6: After paying the course fee, candidates can access the video lectures and reading material from the course page. Free audit option does not provide access to graded assignments.

Step 7: The learners can download video lectures from the course on their device to watch later and complete the course at their own pace.

The Syllabus

Videos

Welcome to Bayesian Statistics: Mixture Models
Installing and using R
Basic definitions
Mixtures of Gaussians
Zero-inflated mixtures
Hierarchical representations
Sampling from a mixture model
The likelihood function
Parameter identifiability

Readings

An Introduction to R
Example of a bimodal mixture of Gaussians
Example of a unimodal and skewed mixture of Gaussians
Example of a unimodal, symmetric and heavy tailed mixture of Gaussians
Example of a zero-inflated negative binomial distribution
Example of a zero-inflated log Gaussian distribution
Sample code for simulating from a Mixture Model

Practice Exercises

Basic definitions
Mixtures of Gaussians
Zero-inflated distributions
Definition of Mixture Models
The likelihood function
Identifiability
Likelihood function for mixture models

Videos

EM for general mixtures
EM for location mixtures of Gaussians
EM example 1
EM example 2

Readings

Sample code for EM example 1
Sample code for EM example 2

Videos

Markov Chain Monte Carlo algorithms part 1
Markov Chain Monte Carlo algorithms, part 2
MCMC for location mixtures of normals Part 1
MCMC for location mixtures of normals Part 2
MCMC Example 1
MCMC Example 2

Readings

Sample code for MCMC example 1
Sample code for MCMC example 2

Videos

Density estimation using Mixture Models
Density Estimation Example
Mixture Models for Clustering
Clustering example
Mixture Models and naive Bayes classifiers
Linear and quadratic discriminant analysis in the context of Mixture Models
Classification example

Readings

Sample code for density estimation problems
Sample EM algorithm for clustering problems
Sample EM algorithm for classification problems

Videos

Numerical stability
Computational issues associated with multimodality
Bayesian Information Criteria (BIC)
Bayesian Information Criteria Example
Estimating the number of components in Bayesian settings
Estimating the full partition structure in Bayesian settings
Example: Bayesian inference for the partition structure

Readings

Sample code to illustrate numerical stability issues
Sample code to illustrate multimodality issues 1
Sample code to illustrate multimodality issues 2
Sample code: Bayesian Information Criteria
Sample code for estimating the number of components and the partition structure in Bayesian models

Practice Exercises

Computational considerations for Mixture Models
Bayesian Information Criteria (BIC)
Estimating the number of components in Bayesian settings
Estimating the partition structure in Bayesian models

Instructors

UC Santa Cruz Frequently Asked Questions (FAQ's)

1: What to do in case of queries/doubts?

Candidates can visit the student discussion forum for asking any doubts to the peer attending the same course. It is available 24x7 and mostly every doubt can be discussed here.

2: What is included in the course fee?

Candidates who purchase the course will get video lectures, graded assignments, reading material and also the certificate at the end after the completion of the course.

3: Do i need to attend the campus at any time?

The Bayesian Statistics: Mixture Models certificate Course is an online programme and can be taken from any part of the world through the internet.

4: Can I get a scholarship for this course?

Although there is no course scholarship, Candidates have two options for learning this course. The course is available for learning without a certificate for the free audit option. Secondly, students who are unable to afford the fee can apply for financial aid here: https://www.coursera.org/learn/mixture-models

5: Who teaches the course?

The course will be taught by Abel Rodriguez, Professor of applied mathematics and statistics at the University of California Santa Cruz.

6: What is the duration of this programme?

The Programme takes at least 21 hours to complete. However, since it is a self-paced course, the candidate can decide the amount of time required for doing the course.

7: How do candidates get to access the course content?

Candidates can access course content right after paying the course fee. For free audit mode learners, they can access the content after enrolling. But in the audit mode, graded assignments are not available.

8: How to contact Coursera?

Candidates can contact Coursera through the various modes given here: https://www.coursera.org/about/contact

9: Who is the course offered by?

The certification course in Bayesian Statistics: Mixture Models are offered by University of California Santa Cruz.