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

4.3
estrellas
450 calificaciones
103 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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76 - 97 de 97 revisiones para Robotics: Estimation and Learning

por juha n

Jul 15, 2018

Assignments need some serious revising.

por Dhagash D

Dec 11, 2016

Not deeply explained not for beginneer.

por Troy W

May 16, 2016

Really too short.

por Fredo P C

Mar 17, 2019

Difficult course

por Raunak H

Dec 18, 2017

Meh

por 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.

por 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.

por 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.

por 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.

por 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.

por 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.

por 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.

por Gaurang G

May 06, 2017

Week 2 kalman filter assignment not clear;

Course can be made more clear like Aerial Robotics.

por Nico W

Feb 05, 2017

What's there is ok, but there is only a few minutes of lecture material each week.

por karthik r

Oct 31, 2017

Although the course is structured properly, the lectures are horrible, explanation for kalman filter lasts couple of minutes,while in universities the topic is studied and implemented as thesis over 6 months, week 4 also throws very poor insight on particle filter, week1 and week3 were better explained. I've learnt more from youtube , The lecturers should see how Andrew Ng teaches his courses, he works through the algorithms step by step. I had to painfully finish this course to unlock the capstone project. I do not recommend this course if you are new to robotics.

por Wilmer A R

Jun 09, 2016

A lot of things to improve, specially thr learning courve is from 1 to 100 and a lot of pre knowledge need, your future public is the hobby robotics people who want to expand their knowledge, a litlle more weeks maybe two can increase the likes for the course. Check this one Control of Mobile Robots you can get an example of a good learning curve

por Shaun L

Apr 12, 2018

The professor left all the teaching to his Phd students. The material was not straight forward, and possibly made even more difficult with the lackluster slides and presentation. A pdf explaining the theories would be more helpful.

por Nick L

Sep 04, 2016

Barely any contents in the course. Only a few minutes of lectures, no quizzes and poorly constructed assignments that waste a lot of time. Weeks 2 and 4 have the worst material I've seen in all the courses I've taken until today.

por Rafael C

Jun 18, 2016

You need to have deep knowledge in matlab to get pass the assignments. I have spent more time figuring out how the simulations are implemented that really learning about the target algorithm to implement.

por Daniel C

Nov 25, 2017

Lecture videos are extremely short and often not useful for the assignment. Also, the assignments are poorly written. Week 4 is the worst. I had to finish this course to unlock the last capstone project.

por Joaquin R

Sep 22, 2018

Lack of detailed content, assigments WAY too difficult if you just take into account what was explained.

por ShuYu W

Jun 08, 2016

The assignment is meaningless. lack of instructions.