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
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. 6,634
- 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. 6,634 |
3 months | Rs. 13,268 |
6 months | Rs. 19,903 |
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
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
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
Assignments
- Basic definitions
- Mixtures of Gaussians
- Zero-inflated distributions
- Definition of Mixture Models
- The likelihood function
- Identifiability
- Likelihood function for mixture models
Peer Reviews
- Likelihood function for mixture models
- Simulating from a Mixture Model
Discussion Prompt
- When are mixture models helpful?
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
Peer Reviews
- The EM algorithm for Mixture Models
- The EM algorithm for zero-inflated mixtures
Discussion Prompt
- Mixtures of log-Gaussians
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
Peer Reviews
- Markov chain Monte Carlo algorithms for Mixture Models
- The MCMC algorithm for zero-inflated mixtures
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
Peer Reviews
- MCMC algorithms and density estimation
- Classification
- The EM algorithm and density estimation
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
Assignments
- Computational considerations for Mixture Models
- Bayesian Information Criteria (BIC)
- Estimating the number of components in Bayesian settings
- Estimating the partition structure in Bayesian models
Peer Reviews
- BIC for zero-inflated mixtures
Discussion Prompt
- Simplifying Binder's expected loss function
Instructors
UC Santa Cruz Frequently Asked Questions (FAQ's)
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.
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.
The Bayesian Statistics: Mixture Models certificate Course is an online programme and can be taken from any part of the world through the internet.
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
The course will be taught by Abel Rodriguez, Professor of applied mathematics and statistics at the University of California Santa Cruz.
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.
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.
Candidates can contact Coursera through the various modes given here: https://www.coursera.org/about/contact
The certification course in Bayesian Statistics: Mixture Models are offered by University of California Santa Cruz.