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Вернуться к Robotics: Estimation and Learning

Отзывы учащихся о курсе Robotics: Estimation and Learning от партнера Пенсильванский университет

4.3
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Оценки: 411
Рецензии: 94

О курсе

How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping....

Лучшие рецензии

SS

Apr 07, 2017

Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.

VG

Feb 16, 2017

The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.

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1–25 из 88 отзывов о курсе Robotics: Estimation and Learning

автор: Eduardo K d S

Oct 26, 2016

I wouldn't recommend this course to my worst enemy. There is 0 commitment from the TA and mentor staff. After 4 weeks of course, not a single reply from any of them in the forums.

To make matters worse, the material is very superficial and lacking, the biggest proof of that is that each module is composed of about 4 videos of 5 minutes each! How can you learn anything in 5 minutes? The topics are so complex, there is simply no way to convey their message in just about 5 minutes. I had to search a lot outside of this course to grasp something of the topics covered. Actually, I found way better explained videos on youtube for free.

The assignments of this course are poorly developed and don't reflect what is discussed in the videos well. I repeat myself, the study material was very lacking and consequently not enough for the assignments themselves. I had to spend days coding, debugging and reverse engineering the assignment files to finally be able to pass because they were wrong! They were incomplete and had wrong information. This is not a reverse engineering course... I shouldn't need to do that. Anyway, I did it because there was also no help from any TA to guide me in the right direction.

Long story short, you are way better off checking the syllabus of this course and checking videos on youtube to learn about them, you'll learn way more than with this money grabber course.

автор: Tri W G

Mar 24, 2018

Pretty short course but it is really worth it if you want to learn about SLAM. Just like any other courses in this specialization, help in the forums is really minimum and the course is pretty though, so you have to spend more time to complete the course. Overall it is a great course, at least for me. Thank you for all lecturers.

автор: Janzaib M

Apr 04, 2017

Here I learnt all the building blocks of Probabilistic Robotics and the significance of statistical methods etc to deal with the the non-linear world.

The course content is very concise and to the point. And, the knowledge transferred is well structured.

автор: Louis B

Jun 30, 2019

This lecture is very useful from the perspective of approaching robotics for the first time. I recommend it!

There was a lot of effort to get back to the normal distribution I had studied before, but it was very good.

автор: Shuang S

Apr 07, 2017

Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me. And these method let me know more about map building in SLAM framework.

автор: vincent g

Feb 16, 2017

The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.

автор: Niju M N

Jun 20, 2016

This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .

автор: Abhilash V

Jun 25, 2016

A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .

автор: Adi S

Nov 23, 2019

Really good course. Engaging and relevant content. The assignments push you but test your fundamentals and you end up learning a lot.

автор: Vu N M

Sep 19, 2018

This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...

автор: SHAO G

Dec 14, 2016

It's a great course. Although the assignment is little tough, you will gain a lot after completing it.

автор: Shubham G

Mar 03, 2018

Very succinct lectures which provides necessary foundation to learn advanced localization algorithms.

автор: 李鹏飞

Aug 08, 2017

It's a really great course and I learn a lot of things which helps me get started with this subject!

автор: KALVAPALLI S P K

Aug 29, 2019

week 2 and 4 needs more information. Yet great learning experience at affordable price.

автор: Lieke V

Jun 13, 2016

Contents relevant, lectures well paced and clear and TA's very helpful and on it!

автор: Mingrui Z

Mar 25, 2018

Good materials for beginners. Assignments are interesting and useful.

.

автор: Dilshan M

Feb 01, 2020

Excellent course in estimation and implementation of Kalman Filters.

автор: Shounak D

May 23, 2018

good course ..expecting more follow up courses on this topic !

автор: akshay s

Nov 02, 2016

Really nice course with a lot of good content.

автор: Akhilesh K

Sep 06, 2017

Challenging but great course to learn.

автор: Guillermo C

Aug 21, 2017

Challenging and very well delivered.

автор: Jianxin L

Oct 13, 2017

It is a good course, like it.

автор: Aryan A

Sep 21, 2018

Great course learnt a lot !!

автор: Utku K

Oct 11, 2016

Very good and informative.

автор: 丘广俊

Feb 23, 2017

It make me to know more!