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.

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 :-)

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.

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

Great course, if a little bit outdated today.

Great course and really helpful for me.

I like professor Rao's commentary.