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Opiniones y comentarios de aprendices correspondientes a Robotics: Estimation and Learning por parte de Universidad de Pensilvania

4.3
estrellas
455 calificaciones
104 revisiones

Acerca del Curso

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....

Principales revisiones

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.

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51 - 75 de 98 revisiones para Robotics: Estimation and Learning

por Daniele M

Aug 30, 2020

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

por Xiaotao G

Dec 16, 2018

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

por 官天河

Dec 11, 2016

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

por Kevin R

Oct 11, 2016

more mathematical depth would be great, videos are too concise

por Raphael C

Jun 25, 2017

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

por Sabari M

Aug 18, 2019

Indepth explanation could be very useful.

por Stephen S

Jun 03, 2016

Good intro to Kalman filters.

por vahini

Nov 17, 2016

it was a good course

por DEEPAK K P

Apr 25, 2019

Good

por 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.

por 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.

por 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.

por 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.

por 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.

por 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).

por 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.

por 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.

por 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..

por Nikita R

Jun 06, 2020

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

por Guining O

Feb 18, 2019

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

por Qiu Q

Sep 12, 2016

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

por Saif

Jun 20, 2016

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

Course Content was good.

por ADITYA N

May 03, 2020

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

por 陈旭展

May 17, 2016

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

por Alex F

Feb 04, 2020

Good programming exercises but very bad lectures