Great course! Parts were very challenging and the supplementary were useful to augment the lectures. The programming was at the right level. Some course links were outdated. I decided to purchase the texts and I am glad I did. I would have appreciated a back button to review material when I got a quiz question incorrect. Overall, great course and I look forward to taking another to build on.

Great course with great lectures given by great people. I liked the variety of topics in the course and all the fun little jokes and trivia offered in the lectures. The quizes were of fairly high level for me, so I really feel like I've learned something new. I would have liked to have exercises before trying the quiz though. And after the quiz it was hard to see what went wrong.

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.

As a student who has learned a variaty of AI works in modern times, this course is too old to be learn. But I have to say it does provide some knowledge about how those scientist try to interpret how our neuro works, which I believe will still be use in the future after we discover more hidden information of our brain.

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).

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.

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.

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.

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.

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.

Provides a great basic understanding of Compuational Neuroscience, and doesn't shy away from the interesting math and programming. While I don't have a comprehensive understanding yet, I now know some key topics and resources to continue learning.

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 :-)

It was a little difficult to get all the mathematical concepts in such a short time, but I really enjoyed the course and it gave me a good insight of what computational neuroscience encompasses.

Thank you for a wonderful course!

Maybe adding more coding examples during course videos might be useful to get a better understanding of how to implement the concepts. Quiz code questions are good for that, but maybe more guided examples might be great.

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.

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.

This course has taught me a lot about computational neuroscience, but it is definitely not at a basic level. I spent a lot of time trying to understand some of the concepts that were explained.

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

The knowledge is not easy to understand at first; however, it is really necessary and useful. I will have it reviewed! Many thanks to the lecturers and the team who created this course.

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.

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

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.

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

Great background in mathematical models and theories. Great explanations. However, the assignments are not clear and some hints are misleading.

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