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Robotics: Estimation and Learning(으)로 돌아가기

펜실베이니아 대학교의 Robotics: Estimation and Learning 학습자 리뷰 및 피드백

4.3
별점
450개의 평가
103개의 리뷰

강좌 소개

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....

최상위 리뷰

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.

필터링 기준:

Robotics: Estimation and Learning의 97개 리뷰 중 51~75

교육 기관: Raphael C

Jun 25, 2017

Good course, videos from week 2 and 4 could be better

교육 기관: Sabari M

Aug 18, 2019

Indepth explanation could be very useful.

교육 기관: Stephen S

Jun 03, 2016

Good intro to Kalman filters.

교육 기관: vahini

Nov 17, 2016

it was a good course

교육 기관: DEEPAK K P

Apr 25, 2019

Good

교육 기관: Francesca G

Jun 16, 2020

I think that this course has a great potential because, through a practical approach, it offers a nice introduction on the fundamentals of the robotic estimation. Regarding the lessons it would be very useful to have detailed references to study in deep the weekly topics and, in particular for week 2 and 4, I suggest to review the materials giving more care to key aspects and removing typos. I appreciate the focus on the assignments (that always gave me the chance to really understand the lessons) but more care in avoiding possible confusion about frames or other details related to the input data can help people to focus on the solution of the problem. I appreciate in week3 assignment the possibility to know if the solution is ok before submitting the result file.

교육 기관: 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.

교육 기관: Ben A

May 11, 2020

This class does a decent job of describing the theoretical / mathematical aspects of Gaussian modelling, kalman filters, occupancy grid mapping, and particle filters. However, it doesn't do such a good job with the practical / implementation details of these topics. The videos are very short, distilled down to only the essential information. I had to seek external sources for further reading to complete two of the programming assignments. Also, TA help on the forums could be quicker / more responsive.

교육 기관: pansi

Apr 20, 2020

This course makes a good introduction to estimation and learning techinques in robotics, and provides good assignments for students to practise. However, there are many drawbacks as well. The time of each lesson is too short, most of them are no more than ten minutes. It's apparently not enough to make students understood clearly. What's more, all lessons are taught by students, not by teachers. There are so many mistakes in the lectures, which gives students bad experiences.

교육 기관: 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.

교육 기관: Rishabh B

Jun 25, 2016

Course contents are very short and to the point. I thought weeks on Gaussian Model Learning and Robot Mapping were neat. But the other two weeks on Kalman filter and Particle Localization were little disappointing. They could have discussed both these topics properly by investing more time. Couple of Assignments are tough and there will be very little help to complete it but nevertheless it will keep you interested in the course.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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).

교육 기관: Fabio B

Aug 17, 2017

Not an easy course, very difficult for beginner students. I considered myself an advanced student (have a PhD in the field) and even I found it difficult sometimes. In any case it is an excellent course.

교육 기관: Gasser N

Sep 12, 2019

this course is great but i felt that the staff are assuming that we know a lot about probability which is not correct , week 4 is very poor and it's very hard to understand it ,hope they can fix this.

교육 기관: Iftach

Oct 29, 2016

need more lectures. there are complicated topics with weak background for the students.

except that it is a great course. thanks..

교육 기관: Nikita R

Jun 06, 2020

Very little lecture material needed to find a lot of additional information to fully understand the presented concepts.

교육 기관: Guining P

Feb 18, 2019

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

교육 기관: Qiu Q

Sep 12, 2016

This course is very useful and interesting, but the materials of week 2 & 4 is enough for their quizs.

교육 기관: Saif

Jun 20, 2016

Poor structuring of assignments. Unclear objectives and wrong input data.

Course Content was good.

교육 기관: ADITYA N

May 03, 2020

Wish had a proper explanation and more detailed derivations or understanding of basics

교육 기관: 陈旭展

May 17, 2016

Who teaching us is a student, and the assignment is not in detail as other class

교육 기관: Alex F

Feb 04, 2020

Good programming exercises but very bad lectures

교육 기관: Damoun L

Feb 18, 2017

very minimal presentation of many concepts!