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Kursteilnehmer-Bewertung und -Feedback für Computational Neuroscience von University of Washington

4.7
542 Bewertungen
124 Bewertungen

Über den Kurs

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....

Top-Bewertungen

JB

May 25, 2019

I really enjoyed this course and think that there was a good variety of material that allowed people of many different backgrounds to take at least one thing away from this.

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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101 - 123 von 123 Bewertungen für Computational Neuroscience

von Akshay K J

Aug 17, 2017

Overall - A good introductory course. But the last week, reinforcement learning and neural networks, could have involved programming questions.

von 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.

von Vanya E

Jul 09, 2017

Great overview of a really cool field, gives nice intuitions for ideas in computational neuroscience.

von 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.

von 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.

von Peter K

May 30, 2017

Great course introducing fundamental concepts in computational neuroscience. People with weak mathematical background can master it although from time to time some more clarification could be helpful. Thanks so much for providing this :-)

von Hui L

Feb 26, 2017

interesting instructor and interesting content. Now I know more about the theoretical research related to neuro function and its connection to machine learning now.

von 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.

von 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.

von Jeff C

Nov 14, 2016

In general very good, but some concepts are rushed over due to the short length of the course.

von Huzi C

Feb 14, 2017

Great course and really helpful for me.

von 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.

von 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.

von Cezary W

Sep 27, 2017

Quite interesting. I would see more explanation of some phenomena, though.

von 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.

von shiyang t

Jul 29, 2019

Being a high school student with zero background in computer programming, i find this course a bit hard.

von Beatriz B

Aug 03, 2019

In my opinion, the course level ought to be intermediate, not beginner. You can take more out of the course if you already have knowledge in this, or related, areas.

von Patricia R

Aug 14, 2019

Interesting but too complicated for beginners

von Bartłomiej L

May 23, 2017

I love the information that has been given there. The problem I have with the course (and it's a big one) is that you start each lecture with great detail and then go to what exactly you want to achieve. Because of this it was easiest for me to understand when I watched all the videos each week and then went backward to get it.

I highly recommend "start with why" videos and book by Simon Sinek, it might give you some info on how to make the lectures more comprehensive.

Having said this - the merit is great and I love having the knowledge. It is just that it isn't well laid out.

von Christopher L M

Jun 03, 2017

[3.5 stars] The course provides an overview of some interesting topics. I would have prefer more emphasis on applications, perhaps in the form of additional exercises. Overall, I have my adventure hat on and I am excited to push on further into the neuro-jungle.

von Renjith B

Feb 20, 2018

I just started the course. But it is exciting for me as a Machine learning and deep learning practitioner!!!

After week 1, the learning curve is steep. The topics are exciting but lectures are not engaging.

von Julia G

Oct 01, 2017

The professors who teach the course are very engaging and are able to make a challenging topic into something interesting and entertaining. The course is very math and programming-heavy, so make sure to brush up on these concepts and be prepared to know how to conceptualize neuronal behavior with mathematical equations and programming functions and vice versa. If you have any questions to post on the Discussion Forum, be prepared to look for the answer outside of the course - the response times of the mentors or other students are horrendous and there are even some instances where questions are never answered. This, as a biology person who had little to no experience in programming or advanced calculus, was the most frustrating aspect of the course. In regards to computational neuroscience as a course, the material itself is beginner-level, but the math/programming is definitely not (more like intermediate/advanced).

von Jiazhi G

Aug 20, 2017

Nice content. Opens the gate of CNS for me.

But some explanations are just too virtual to be understood easily.

At last few weeks, the course talks about the relationship between NS and ML, which is astonishing.