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Robotics: Estimation and Learning, Пенсильванский университет

4.1
Оценки: 316
Рецензии: 82

Об этом курсе

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....

Лучшие рецензии

автор: 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.

автор: NN

Jun 20, 2016

This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .

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Рецензии: 76

автор: Guining Pertin

Feb 18, 2019

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

автор: Aman Bawa

Feb 12, 2019

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

автор: pavana abhiram Sirimamilla

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.

автор: davidjameshall

Jan 07, 2019

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

автор: Liang Li

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.

автор: Xiaotao Guo

Dec 16, 2018

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

автор: Joaquin Rincon

Sep 22, 2018

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

автор: Aryan Agarwal

Sep 21, 2018

Great course learnt a lot !!

автор: Vu Nhat Minh

Sep 19, 2018

This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ...

автор: Yuanxuan Wang

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).