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Вернуться к Вычислительная нейробиология

Отзывы учащихся о курсе Вычислительная нейробиология от партнера Вашингтонский университет

4.7
звезд
Оценки: 662
Рецензии: 155

О курсе

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....

Лучшие рецензии

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.

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.

Фильтр по:

126–150 из 153 отзывов о курсе Вычислительная нейробиология

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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 :-)

автор: Diego J V (

Feb 20, 2017

This course serves as a nice introduction to the field of computational neuroscience. However, at some points, more than basic knowledge of differential equations and probability & statistics is needed.

автор: Gustavo S d S

Nov 15, 2016

Learnt concepts about Neural Networks, Supervised / Unsupervised / Reinforcement Learning. Covers topics about Information Theory, Statistic and Probability. Matlab / Python assignments.

автор: 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.

автор: 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.

автор: Mark A

Jul 13, 2017

A good look at mathematical models focusing mainly at the synapse and neuron level. The math came a little fast and furious for my 30+ years antique math training.

автор: Anurag M

Feb 03, 2019

Starts off great but get rushed 3/4ths into the course. Too much content, too little explanation, but recovers swiftly to end on a high.

Recommended

автор: 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.

автор: Driss A L

Dec 02, 2018

As a self-paced student, I like this kind of course. I hope to see a whole specialization in this field with final capstone project. Thanks.

автор: Pho H

Dec 28, 2018

Pretty good. A bit of mathematical ambiguity and lax notational conventions, but the course content was solid and presented clearly.

автор: Serena R

Aug 31, 2017

I found this course helpful and inspiring for my research activity. I suggest it to anyone who has basic mathematical skills.

автор: Erik B

Aug 25, 2019

Overall I enjoyed this class, but towards the end it gets more into machine learning and away from the neuroscience.

автор: Vanya E

Jul 09, 2017

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

автор: Jeff C

Nov 14, 2016

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

автор: Gaugain G

Dec 19, 2019

Très bon cours je recommande pour tous les gens intéressé par les neurosciences théoriques

автор: 徐锦辉

Sep 01, 2019

A better tittle for this course is 'From neuroscience to artificial intelligence'.

автор: Cezary W

Sep 27, 2017

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

автор: Renaldas Z

Jun 30, 2017

Great course, if a little bit outdated today.

автор: Huzi C

Feb 14, 2017

Great course and really helpful for me.

автор: Yonghee B

Feb 29, 2020

Best for the beginner.