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Robotics: Estimation and Learning(으)로 돌아가기

펜실베이니아 대학교의 Robotics: Estimation and Learning 학습자 리뷰 및 피드백

4.3
408개의 평가
92개의 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 86개 리뷰 중 51~75

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: 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: 陈旭展

May 17, 2016

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

교육 기관: Damoun L

Feb 18, 2017

very minimal presentation of many concepts!

교육 기관: juha n

Jul 15, 2018

Assignments need some serious revising.

교육 기관: Dhagash D

Dec 11, 2016

Not deeply explained not for beginneer.

교육 기관: Troy W

May 16, 2016

Really too short.

교육 기관: Fredo P C

Mar 17, 2019

Difficult course

교육 기관: Raunak H

Dec 18, 2017

Meh

교육 기관: Enrico A

Jul 29, 2017

The material covered is very interesting. However, I am a bit disappointed by the lecture format and the assignment preparation. It is good to have concise lectures that stick to the core of the subject. However, in this case, they were not very clear. Additionally, the assignments tend to be cover different material from the lectures. Besides, they are not well explained and it is difficult to understand what is required. You basically end up doing a lot of trial and error. Luckily, the blog contains very useful posts from other frustrated users.

교육 기관: Behrooz S

Jun 10, 2016

Very important materials are explained super briefly. I would only suggest it for getting familiar with the estimation "keywords and terminologies" or for someone who wants to brush up his/her prior knowledge in estimation. The total session time for all 4 weeks together is only a few hours and the homeworks do not cover the session topics.

교육 기관: 李晨曦

Jul 30, 2017

The lectures does not provide enough information and dig into the underlying principles. Lectures that are supposed to be half an hour are condensed into several minutes. Of all the courses in this series, I rely on external resources and forums the most to finish this one. I honestly think the teaching staff could do a better job.

교육 기관: 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.

교육 기관: Tim O

Dec 10, 2016

When I took, assignments 2 and 4 were broken and there were no mentors to help students. However, I am now told they will be fixing the course. I give 2 stars becuase the concepts of the assignments is good, but the course needs more attention.

교육 기관: Yiming Z

Oct 15, 2017

Poor explanations in the lectures especially for particle filter.

It doesn't go deep into why and how the method was developed in a theoretical way.

교육 기관: Alejandro A V

Jun 08, 2016

It is not very clear. The assignments have several problems with the given code. There are many things to improve in the next sessions.