About this Course

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Start instantly and learn at your own schedule.

Flexible deadlines

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Intermediate Level

Approx. 11 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.

Intermediate Level

Approx. 11 hours to complete

English

Subtitles: English

Offered by

EIT Digital  logo

EIT Digital

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

Module 1: Computational Tree Logic

3 hours to complete
6 videos (Total 61 min), 3 readings, 4 quizzes
6 videos
Introduction13m
Semantics of CTL13m
Model Checking CTL9m
The Until Operator12m
The Always Operator9m
3 readings
Script 1 and 2.110m
Script 2.2 and 2.310m
Script 2.420m
4 practice exercises
Formulate for yourself6m
Test your understanding of CTL semantics14m
Check your understanding of CTL20m
Model checking eventually, always and until22m
Week
2

Week 2

2 hours to complete

Discrete Time Markov Chains

2 hours to complete
5 videos (Total 49 min), 2 readings, 5 quizzes
5 videos
Evolution in Time13m
Transient probabilities9m
State classification5m
Steady-state probabilities12m
2 readings
Script 3.1 and 3.210m
Script 3.310m
5 practice exercises
Evolution of DTMCs6m
Compute transient probabilities10m
Classification of DTMC states True or False?14m
State classification16m
Steady-state computation12m
Week
3

Week 3

2 hours to complete

Probabilistic Computational Tree Logic

2 hours to complete
5 videos (Total 36 min), 3 readings, 6 quizzes
5 videos
Model checking and the Next operator7m
Time-bounded Until6m
Backwards computation4m
Unbounded Until8m
3 readings
Script: 4.1 and 4.210m
Script: 4.3.1 and 4.3.225m
Script 4.3.310m
6 practice exercises
PCTL Syntax8m
Checking PCTL next4m
Test your understanding of PCTL Until6m
Checking time-bounded until16m
Checking unbounded until10m
Test your understanding of PCTL6m
Week
4

Week 4

2 hours to complete

Continuous Time Markov Chains

2 hours to complete
5 videos (Total 57 min), 2 readings, 6 quizzes
5 videos
Generator matrix11m
Steady-state probabilities11m
Triple Modular Redundancy11m
Uniformisation12m
2 readings
Script: 5.1 and 5.220m
Script: 5.315m
6 practice exercises
Generator matrix6m
Test your understanding of CTMCs6m
Steady state probability in CTMCs10m
Identifying BSCCs12m
Test your understanding of Uniformisation6m
Uniformisation12m

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.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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