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Learner Reviews & Feedback for Quantitative Model Checking by EIT Digital

4.2
stars
52 ratings

About the Course

Welcome to the cutting-edge course on Quantitative Model Checking for Markov Chains! As technology permeates every aspect of modern life—Embedded Systems, Cyber-Physical Systems, Communication Protocols, and Transportation Systems—the need for dependable software is at an all-time high. One tiny flaw can lead to catastrophic failures and enormous costs. That's where you come in. The course kicks off with creating a State Transition System, the basic model that captures the intricate dynamics of real-world systems. Soon you'll step into the world of Discrete-time and Continuous-time Markov Chains—powerful mathematical formalisms that are versatile enough to model complex systems yet elegant in their design. These aren't just theories; they are tools actively used across various domains for performance and dependability evaluation. But we won't stop at modelling. The heart of this course is 'Model Checking,' a formal verification method that scrutinizes the functionality of your system model. Learn how to express dependability properties, track the evolution of Markov chains over time, and verify whether states meet particular conditions—all using advanced computational algorithms. By the end of this course, you'll be equipped with the skills to: - Specify dependability properties for a range of transition systems. - Understand the temporal evolution of Markov chains. - Analyze and compute the satisfaction set for multiple properties. Are you ready to become an expert in ensuring the reliability of tomorrow's technologies? Click here to Enroll today and join us in mastering the art and science of model checking....

Top reviews

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1 - 8 of 8 Reviews for Quantitative Model Checking

By Carl H

Feb 1, 2019

Lectures are rushed and not explained well. Discussion forms seem to be filled with "Is there inaccuracy in in quiz X". Here is a direct quote from one of the discussions "I actually didn't use the formula from the lecture but from the cited paper by Baier et. al. - same stuff. works for 11, doesn't for 12". Granted that the subject matter this course covers is difficult, I feel like this course makes it harder rather than easier. I wouldn't recommend it to anyone.

By ElissaHu

Apr 27, 2018

difficult in week4&week5, but interesting

By Joseph V

Jan 19, 2018

The lectures on Coursera are nice, but please remove to cringe parts (any outside shot video material).

Overall the course is very bad because the tele lectures are very bad quality and thus does not motivate you at all to keep track of the course during the period. Recommendation: Ditch tele lectures all together and give actual bonus points for completing Coursera parts on time.

By ref a

Aug 27, 2023

It's not my specialty, just thank you

Thanks

It's not my specialty, just thank you

Thanks

It's not my specialty, just thank you

Thanks

By 潘临杰

Aug 24, 2017

模型检测入门教程,学完课程之后对于模型检测有了直观的认识。

By Mario G S

Sep 16, 2017

Very good course!!!

By Алина Г

Mar 21, 2022

Some quizs have a mistakes and typos.

No useful example for quiz in the last week.

By Hao L

Jun 1, 2020

I can tell the course team did put a lot of time and energy to make this course and I am thankful for that. I have also learned a lot through this course. However, the defects make the abstract course even harder to follow: mistakes are not rare thorough the whole course; lacking involvement in the discussion forum from the team and the illustration can be sometimes hard to understand. Prepare to suffer if you want to learn this course along especially without any previous background (time consumption should be expected to be ~5hours/week).