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学生对 宾夕法尼亚大学 提供的 Robotics: Estimation and Learning 的评价和反馈

4.2
371 个评分
85 个审阅

课程概述

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....

热门审阅

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.

NN

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 .

筛选依据:

1 - Robotics: Estimation and Learning 的 25 个评论(共 80 个)

创建者 Hussain M A

Jun 07, 2019

Course content needs researching on the internet as well. And course assignments are good learning experience but need research too.

创建者 DEEPAK K P

Apr 25, 2019

Good

创建者 Bálint - H F

Mar 20, 2019

Great ! Difficult !

创建者 Fredo P C

Mar 17, 2019

Difficult course

创建者 Guining P

Feb 18, 2019

Some more help or examples should have been provided for the programming exercises, especially the last one

创建者 Aman B

Feb 12, 2019

It was a well timed course with short videos. However, the assignments didn't do justice (especially assignment 4)

创建者 pavana a S

Feb 10, 2019

It is a good course and I learnt a lot. However, Professor should have taught instead of the TAs. 4 or 5 minute lectures on important concepts such as particle filter and Kalman Filter is not at all adequate. Wrong formula is shown for one of the important concepts (particle filter). I hope they work on improving the course.

创建者 davidjameshall

Jan 07, 2019

Excellent exposure to mapping, localization, etc. Would have liked to have odometry included in the week4 assignment.

创建者 Liang L

Dec 31, 2018

I don't think the staff and the mentors organize the course materials well. Firstly, they don't introduce the concepts clearly in the videos, and the professor is hardly involved. Secondly, the programming assignments are not carefully designed, as there is not clear statement and an expected outcome to examine our work. I suggest watching Andrew Ng's Machine Learning to see how well he and his team organize the course materials.

创建者 Xiaotao G

Dec 16, 2018

the topic is interesting, but the videos seems a little bit short

创建者 Joaquin R

Sep 22, 2018

Lack of detailed content, assigments WAY too difficult if you just take into account what was explained.

创建者 Aryan A

Sep 21, 2018

Great course learnt a lot !!

创建者 Vu N M

Sep 19, 2018

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

创建者 Yuanxuan W

Aug 15, 2018

Good course schedule, but videos in week 2 and week 4 really need some rework. There are errors in slides and videos are too vague to be helpful, I have to look for external materials to understand the topics (Kalman Filter and Particle Filter).

创建者 Juan Á F M

Aug 04, 2018

All in all, it's a very interesting, absolutely necessary topic for robotics. But everything is treated here without theory tests, detailed examples and the like, so learning is only tested with programming tasks. The student must work a lot with MATLAB to come up with crafty solutions for week practices.

创建者 juha n

Jul 15, 2018

Assignments need some serious revising.

创建者 Saurabh M

Jul 06, 2018

The course structure is nice. However there is little explanation for the programming assignments, especially the last one (week 4). For other weeks I got good help from the forums however the forums do not have much threads and many are unanswered. It would be great if more reading material can be added for that week.

创建者 Shounak D

May 23, 2018

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

创建者 Matthew P

May 14, 2018

This course covers some very important techniques in modern robotics including Kalman filters, mapping, and Particle filters. However, the way that these topics are presented in this course is not very clear. The later lectures especially lack the necessary content to provide a clear understanding of advanced topics. The final assignment in particular is very poorly documented and the included instructions are a bit misleading. In addition to that, the forums seem to have been abandoned by the course instructors and are full of unanswered questions from struggling students, some of them more than a year old. This course needs some serious attention and revision. Definitely the lowest quality course of this series.

创建者 Shaun L

Apr 12, 2018

The professor left all the teaching to his Phd students. The material was not straight forward, and possibly made even more difficult with the lackluster slides and presentation. A pdf explaining the theories would be more helpful.

创建者 Terry Z

Apr 03, 2018

The assignment is not designed very well especially the last one. Lacking of lots of details.

创建者 Abdulbaki

Mar 28, 2018

First 2 Week was very good but I think Mapping and Localization parts were not covered throughly. There should have been more detail.

创建者 Mingrui Z

Mar 25, 2018

Good materials for beginners. Assignments are interesting and useful.

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创建者 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.

创建者 Shubham G

Mar 03, 2018

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