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學生對 宾夕法尼亚大学 提供的 Robotics: Estimation and Learning 的評價和反饋

4.3
485 個評分
115 條評論

課程概述

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....

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SS
2017年4月6日

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
2017年2月15日

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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51 - Robotics: Estimation and Learning 的 75 個評論(共 109 個)

創建者 davidjameshall

2019年1月7日

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

創建者 Aman B

2019年2月12日

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

創建者 Ramachandran S

2017年4月23日

Pretty practical course It' ll involve a good amount of programming. Not quiz and theoretical verification here.

創建者 Terry Z

2018年4月2日

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

創建者 Daniele M

2020年8月30日

great assignments and lecture... would suggest to provide more readings...

創建者 Xiaotao G

2018年12月16日

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

創建者 官天河

2016年12月11日

Everything is good,but the assignments are a little hard,haha

創建者 Kevin R

2016年10月11日

more mathematical depth would be great, videos are too concise

創建者 Raphael C

2017年6月25日

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

創建者 Sabari M M

2019年8月18日

Indepth explanation could be very useful.

創建者 Stephen S

2016年6月3日

Good intro to Kalman filters.

創建者 vahini

2016年11月17日

it was a good course

創建者 Christos P

2021年1月2日

Too short.

創建者 Deepak P

2019年4月25日

Good

創建者 pansi

2020年4月20日

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.

創建者 Liang L

2018年12月30日

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.

創建者 Rishabh B

2016年6月25日

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.

創建者 Abhiram S

2019年2月10日

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.

創建者 Saurabh M

2018年7月6日

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.

創建者 Yuanxuan W

2018年8月15日

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).

創建者 Fabio B

2017年8月17日

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.

創建者 Gasser N

2019年9月12日

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.

創建者 Iftach F

2016年10月29日

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

except that it is a great course. thanks..

創建者 Nikita R

2020年6月6日

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

創建者 Erick A M D

2021年1月11日

The videos shall last more. The subject of each week is very interesting, but the explanation are very short.