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.
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.
교육 기관: Sourabh J•
Nov 05, 2016
교육 기관: Ivy T•
Oct 26, 2017
I'm a professor in psychiatry with a background in clinical psychology. I conduct clinical research to understand the neural mechanisms involved in psychiatric diseases. I found the course very informative and covers topics in computational neuroscience that are critical to further my research in the computational direction.
The course involves a moderate amount of math, which is absolutely necessary to understand the materials. For someone like me who did calculus more than 20 years ago (i.e., rusty), I often found the explanation of the math too fast. I had to pause the videos multiple times to digest the formulas and re-watch some videos to get a true understanding of the materials in order to complete the quizzes successfully (especially in later weeks as the concepts get more advanced). The supplementary tutorials by Rich Pang are extremely helpful. He talks at a slower pace, allowing time for you to think along the way. He is also very good at helping you to get an intuitive understanding of the complex concepts. I would recommend watching Rich's tutorials before watching the lecture videos. That way, you would understand the lectures more readily.
The quizzes are overall well designed and helpful in terms of facilitating the consolidation of your understanding of the concepts and methods covered in that particular week. I don't know if it's just me. I tended to spend a lot more time than the estimated time (e.g., 3 hours instead of 1 hour) to complete and pass a quiz (especially later ones that involve more Matlab programming).
Overall, I found this course very useful and overall well constructed.
교육 기관: Claudio G•
May 22, 2018
I have really liked this course,but there is a lot of statistics I didn't expect to find at the beginning. Ihave given me exactly the flavor of what Computational Neuroscience is and what are the field of applications, which are REALLY interesting. Honestly I have found a bit too condensed the part regarding the description of "cause" and all the related statistic stuff which I think should deserve some 1 or 2 videos with solved problems. All summed up, I think this course is really worth of taking. Best regards to the professors and to the mentors and to those who have given me a lot of help with their posting on the forum. Their doubts and the relative answers have really been enlightening for driving me towards a better understanding of the matter. Thank you to all of you.
교육 기관: 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.
교육 기관: Moustapha M A•
May 26, 2018
The course over all was very good but I didnt given it five because of the following : in course 2-5 the lectures were not coherent and the there was no expalantion for how certain experiments or measurments were done and hence natural progression to associate the mathematics. The lecturer tends to speak fast and sometimes eat her words so there was absence of clarity . The lectures were not well structured . on the otherhand lectures 6-8 were much clearer in presenation and scope and more linked with the quizes.
교육 기관: Steven P•
Nov 14, 2019
Really interesting overview of the concepts, math and coding necessary to understand how neurons work. The lectures are hit and miss when it comes to explaining the content, a majority of the lectures focused on derivatives and mathematical concepts which lost me. The supplementary videos, especially with Rich were really valuable and helped to synthesize some of the content. Felt like there was a ton of information packed into this course, just not all completely applicable.
교육 기관: 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.
교육 기관: Shengliang D•
Jan 18, 2020
The contents are well organized and arranged corresponding to the textbook Theoretical Neuroscience. There are supplementary materials for the lecture of each week. The assignments are very helpful for understanding the lectures, with code and data for Matlab, Python 2 and Python 3, which is very friendly for people who are only familiar with some of them. It would be better if the assignments could cover more about the lecture.
교육 기관: 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.
교육 기관: Víthor R F•
Mar 10, 2018
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).
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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 :-)
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.