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Avis et commentaires pour d'étudiants pour Neurosciences computationnelles par Université de Washington

4.6
étoiles
772 évaluations
181 avis

À propos du cours

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information....

Meilleurs avis

AG

Jun 11, 2020

Brilliant course. For a HS student the math was challenging, but the quizzes and assignments were perfect. The tutorials and supplementary materials are super helpful. All in all, I loved it.

CM

Jun 15, 2017

This course is an excellent introduction to the field of computational neuroscience, with engaging lectures and interesting assignments that make learning the material easy.

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126 - 150 sur 179 Avis pour Neurosciences computationnelles

par Vili V

Jul 28, 2019

Very enjoyable course!

par 刘仕琪

Mar 11, 2017

The teacher is funny!

par Cian M M

Oct 06, 2019

Very nice indeed!

par Gavin J J

Sep 12, 2017

Its an eye opener

par Lalitha A S R A

Sep 27, 2020

Excellent course

par Palis P

Jun 14, 2020

Just amazing! :)

par Bilal C

Apr 12, 2017

I recommend it

par Mtakuja L

Apr 03, 2017

Nice course !

par 钱琨浔

Apr 28, 2017

very helpful

par Sourabh J

Nov 05, 2016

Good course!

par Jacob D

Oct 20, 2020

This topic combines a lot of what I find interesting so I am grateful this course exists. Before I started it, my hope was to walk away with more familiarity and a solid foundation for computational neuroscience. As far as I can tell, I was in fact able to gain a basic understanding. There were also a lot of really fascinating concepts throughout the course.

My only issue is that some ideas (probabilities and encoding for instance) gave me a lot of trouble and I felt like instructor couldn't explain these things in a way that I could understand. Sometimes they'd just toss a bunch of unnecessary big words and equations or invoke strange conventions that I'm not comfortable with, leaving me struggling behind. To be fair, there were many ideas for which the instructor also gave very helpful examples or made intuitive connections with other ideas. I wish I could elaborate better on the teaching quality, but this is only a review.

Overall, I was exposed to many new interesting concepts. I seriously hope that I might be able to work in a field similar to this one day.

par claudio g

May 22, 2018

I have really liked this course,but there is a lot of statistics I didn't expect to find at the beginning. Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting. Honestly I have found a bit too condensed the part regarding the description of "cause" and all the related statistic stuff which I think should deserve some 1 or 2 videos with solved problems. All summed up, I think this course is really worth of taking. Best regards to the professors and to the mentors and to those who have given me a lot of help with their posting on the forum. Their doubts and the relative answers have really been enlightening for driving me towards a better understanding of the matter. Thank you to all of you.

par Shreyas G

Jul 18, 2020

The course provides a really good insight into the field of computational neuroscience, touching every area possible. The experiments and real-life research work discussed throughout gives a good understanding and exposure to the field. Assignments equip the student well with the necessary skills and thought process in problem-solving while strengthening the concepts as well. However, I found the computational knowledge required for the course demanding. Though there was adequate help available, more could've been helpful, especially in python. It was a great learning experience.

par Aditya A

Mar 28, 2019

I liked the course. I enjoyed solving the problems and I am now confident in learning more advanced concepts and getting my hands dirty in neural networks and machine learning.

I only have one complaint like suggestion, if only the TAs or the instructors could show some examples of solutions or algorithms for the concepts, it would have been much easier. Although, i have understood the concepts, I have not yet grasped the implementations of the concepts in actual codes and programs. Please update the course regarding that. Thanks a lot again to Rajesh, Adrienne and Richard.

par Moustapha M A

May 26, 2018

The course over all was very good but I didnt given it five because of the following : in course 2-5 the lectures were not coherent and the there was no expalantion for how certain experiments or measurments were done and hence natural progression to associate the mathematics. The lecturer tends to speak fast and sometimes eat her words so there was absence of clarity . The lectures were not well structured . on the otherhand lectures 6-8 were much clearer in presenation and scope and more linked with the quizes.

par Steven P

Nov 14, 2019

Really interesting overview of the concepts, math and coding necessary to understand how neurons work. The lectures are hit and miss when it comes to explaining the content, a majority of the lectures focused on derivatives and mathematical concepts which lost me. The supplementary videos, especially with Rich were really valuable and helped to synthesize some of the content. Felt like there was a ton of information packed into this course, just not all completely applicable.

par Wilder R

Jun 28, 2017

I loved the course and the way Professors Rajesh and Adrienne conducted it. I only think the slides and lecture notes could have some more material. I'm a Software Engineer, with a background in Computer Science, but I have been far from math for quite some time (that's why I'm now doing a Cauculus 1 course). I got lost a few times in the quizzes due to lack of information.

But I loved the course and all the new knowledge I acquired. I will certainly recommend. it.

par Shengliang D

Jan 18, 2020

The contents are well organized and arranged corresponding to the textbook Theoretical Neuroscience. There are supplementary materials for the lecture of each week. The assignments are very helpful for understanding the lectures, with code and data for Matlab, Python 2 and Python 3, which is very friendly for people who are only familiar with some of them. It would be better if the assignments could cover more about the lecture.

par Wojtek P

Jul 08, 2017

Extremely interesting subject, many ideas and methods presented. Basic disadvantage is a method of source which is closer to seminar rather than leacture. But, lost of details is acceptable due to a huge amount of material. Advanced mathematics from various areas is necessary to fully understand all the ideas. Anyway, I recommend the course.

par Víthor R F

Mar 10, 2018

Many of the lectures do not make a plenty of sense relative to their quizzes. The lectures are rather theoretical and the quizzes are rather practical. Also, one of the professors have better didactics than the other. Either way, it was quite an adventure (my hat almost didn't survive).

par Manuel P

Dec 15, 2017

I enjoyed the course very much and hopefully learned quite a bit about how to model neurons and some interesting new ways to look at methods like perceptrons and PCA. The course videos are short by very dense. Make sure you make enough notes and prepare enough time for all of them.

par george v

Mar 18, 2017

Very good teaching skills by both professors and interesting guest lectures and tutorials. Assignements that demand your full attention. I would like some more depth as far as the developement of programming skills and the practice. Great intuition and explanation.

par lcy9086

Mar 16, 2018

This course provides you with a brief introduction to computational neural science. You can benefit from it as long as you have basis in calculus and linear algebra. But for those who want to get the best from it, you need to build up your mathematics.

par Krasin G

Nov 16, 2016

This is a very interesting course that provides many interesting ideas. At the same time it is quite challenging. Solid background in probability theory, linear algebra and signal processing is needed. Considering it "Introductory" level is misleading.

par Marek C

Apr 09, 2018

Good introduction to the topic. Course quite easy for engineers, may be quite challenging fro non-engineers. I didn't like quizes - they were too easy and were not provoking too much creative thinking. They were also easier than the lecture material.