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Avis et commentaires pour d'étudiants pour Robotics: Estimation and Learning par Université de Pennsylvanie

4.3
étoiles
490 évaluations
115 avis

À propos du cours

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....

Meilleurs avis

VG

15 févr. 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.

DA

5 févr. 2021

This course was interesting but I think the video material was too shallow and not detailed enough. The assignment for Week 4 was extremely challenging!

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51 - 75 sur 109 Avis pour Robotics: Estimation and Learning

par davidjameshall

7 janv. 2019

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

par Aman B

12 févr. 2019

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

par Ramachandran S

23 avr. 2017

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

par Terry Z

2 avr. 2018

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

par Daniele M

30 août 2020

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

par Xiaotao G

16 déc. 2018

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

par 官天河

11 déc. 2016

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

par Kevin R

11 oct. 2016

more mathematical depth would be great, videos are too concise

par Raphael C

25 juin 2017

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

par Sabari M M

18 août 2019

Indepth explanation could be very useful.

par Stephen S

3 juin 2016

Good intro to Kalman filters.

par vahini

17 nov. 2016

it was a good course

par Christos P

2 janv. 2021

Too short.

par Deepak P

25 avr. 2019

Good

par pansi

20 avr. 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.

par Liang L

30 déc. 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.

par Rishabh B

25 juin 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.

par Abhiram S

10 févr. 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.

par Saurabh M

6 juil. 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.

par Yuanxuan W

15 août 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).

par Fabio B

17 août 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.

par Gasser N

12 sept. 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.

par Iftach F

29 oct. 2016

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

except that it is a great course. thanks..

par Nikita R

6 juin 2020

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

par Erick A M D

11 janv. 2021

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