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Вернуться к Вычислительная нейробиология

Отзывы учащихся о курсе Вычислительная нейробиология от партнера Вашингтонский университет

Оценки: 884
Рецензии: 210

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

10 июня 2020 г.

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.

24 мая 2019 г.

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:

176–200 из 209 отзывов о курсе Вычислительная нейробиология

автор: Chiang Y

30 июля 2020 г.

Pretty comprehensive for beginners, the only drawback is that the course doesn't offer organized ppt or notes for review. Writing notes took me lots of unnecessary time so I suggest a more efficient teaching method.

автор: Diego J V (

20 февр. 2017 г.

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

15 нояб. 2016 г.

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

автор: Beatriz B

3 авг. 2019 г.

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

25 февр. 2017 г.

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

13 июля 2017 г.

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

3 февр. 2019 г.

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.


автор: Akshay K J

17 авг. 2017 г.

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

автор: Driss A L

2 дек. 2018 г.

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

27 дек. 2018 г.

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

автор: Ricardo C

27 окт. 2020 г.

it delivers what it promisses: a first grasp of computational neurosciences, with a good overview of the fundamental concepts.

автор: Serena R

31 авг. 2017 г.

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

автор: Erik B

25 авг. 2019 г.

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

автор: Vanya E

9 июля 2017 г.

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

автор: Avinash T

23 авг. 2020 г.

Very interesting course, gained many skills of modelling that i am going to utilise in my research

автор: Jeff C

14 нояб. 2016 г.

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

автор: Gabriel G

19 дек. 2019 г.

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

автор: Zikou L

13 авг. 2021 г.

Very demanding math and programming, need some basic knoledge of matrix and vectors

автор: 徐锦辉

1 сент. 2019 г.

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

автор: Cezary W

27 сент. 2017 г.

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

автор: Renaldas Z

30 июня 2017 г.

Great course, if a little bit outdated today.

автор: Huzi C

14 февр. 2017 г.

Great course and really helpful for me.

автор: Abhilash C

18 июня 2020 г.

I like professor Rao's commentary.

автор: ­배용희(대학원/일반대학원 물

29 февр. 2020 г.

Best for the beginner.

автор: Rahul V

10 июня 2021 г.

T​his course is at intermediate level and really test your ability to read, re read, go through discussion forums and apply whatever was taught. However, this is an overview of the field and should be supplemented by further readings and exercises. The homework problems were doable with assisted scripts, doing them from scratch was really tough. One can go through the solved exercises from Abbott book and other YouTube videos to get a grip on fundamentals of linear algebra and maths derivations. The TA did a great job to revise and review basics. Many topics like reinforcement learning were just touched upon, but didn't show up in its fury in quizzes! Overall, this course is good to get a taste of this domain. Thank you for taking time and effort to make it available on a public platform