About this Course

18,817 recent views

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 21 hours to complete

English

Subtitles: English

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 21 hours to complete

English

Subtitles: English

Offered by

University of California, Santa Cruz logo

University of California, Santa Cruz

Syllabus - What you will learn from this course

Week
1

Week 1

4 hours to complete

Basic concepts on Mixture Models

4 hours to complete
9 videos (Total 94 min), 7 readings, 9 quizzes
9 videos
Installing and using R5m
Basic definitions25m
Mixtures of Gaussians10m
Zero-inflated mixtures11m
Hierarchical representations7m
Sampling from a mixture model5m
The likelihood function14m
Parameter identifiability10m
7 readings
An Introduction to R45m
Example of a bimodal mixture of Gaussians3m
Example of a unimodal and skewed mixture of Gaussians3m
Example of a unimodal, symmetric and heavy tailed mixture of Gaussians3m
Example of a zero-inflated negative binomial distribution3m
Example of a zero-inflated log Gaussian distribution3m
Sample code for simulating from a Mixture Model10m
7 practice exercises
Basic definitions6m
Mixtures of Gaussians4m
Zero-inflated distributions4m
Definition of Mixture Models20m
The likelihood function
Identifiability
Likelihood function for mixture models4m
Week
2

Week 2

4 hours to complete

Maximum likelihood estimation for Mixture Models

4 hours to complete
4 videos (Total 73 min), 2 readings, 2 quizzes
4 videos
EM for location mixtures of Gaussians22m
EM example 112m
EM example 213m
2 readings
Sample code for EM example 110m
Sample code for EM example 210m
Week
3

Week 3

4 hours to complete

Bayesian estimation for Mixture Models

4 hours to complete
6 videos (Total 84 min), 2 readings, 2 quizzes
6 videos
Markov Chain Monte Carlo algorithms, part 213m
MCMC for location mixtures of normals Part 119m
MCMC for location mixtures of normals Part 214m
MCMC Example 111m
MCMC Example 211m
2 readings
Sample code for MCMC example 110m
Sample code for MCMC example 210m
Week
4

Week 4

5 hours to complete

Applications of Mixture Models

5 hours to complete
7 videos (Total 108 min), 3 readings, 3 quizzes
7 videos
Density Estimation Example10m
Mixture Models for Clustering23m
Clustering example11m
Mixture Models and naive Bayes classifiers21m
Linear and quadratic discriminant analysis in the context of Mixture Models18m
Classification example10m
3 readings
Sample code for density estimation problems10m
Sample EM algorithm for clustering problems10m
Sample EM algorithm for classification problems10m

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.

More questions? Visit the Learner Help Center.