Great

Good!

good

I found this course difficult to get through, even tedious towards the end; this is a fundamentals course after all so it being heavily theoretical was to be expected.

I found the practical assessments challenging and very good for developing the understanding of what had been taught; however one practical in the first week and one in the fourth week was too few. I was longing for the final assignment!

It remains to be seen how relevent this is to the upcoming modules (I do feel that I have a good grounding and understanding of the underlying process so maybe it was a necessary slog). I hope that they are more practical!

Very small observation: the use of bespoke Python packages with the online notebooks was also a bit frustrating. I like to be able to work off line (e.g. in Anaconda) and I also wanted to try and work out some of the challenges in R but without access to the bespoke packages it would have been too involved. I understand that you have a lot of students though and online notebooks are easier to manage.

The instructors are friendly, which creates a pleasant learning atmosphere. However, there is room for improvement in the teaching of mathematical formulas. These are often covered too quickly and not explained sufficiently, making it very difficult to understand. Additionally, I find the constant encouragement to read the book a bit excessive. If reading the book alone is sufficient, there would be no need to attend the course. A more balanced approach between book study and practical explanation in class would be desirable.

Good examples can simplify things greatly. there where several places where an extra step would add value. Some lessons, such as the problem with the trucks could go a little deeper. Assignment grading system is buggy. I spend hours (that I do not have) because I used "transition" as a variable. After I figured this out, I was no longer able to know if other error is due to some other things the Notebook does not like or if there are actual errors. I also posted some questions but never got any response to any of them.

Not much satisfied with the course structure...

To successfully understand and complete this course, you constantly need to refer the reference book.

Most of the students are referring to online courses so that they can learn more efficiently than reading,

any casual book reader can easily complete this course but for the person who like to learn from videos rather than book reading (like me), it was not so great experience.

The content is there and it is good, but teachers lack good teaching skills and lessons feel rushed (Ng lectures come to mind as positive examples of good practices). Also, lessons aren't self-contained, as you need to read the book if you want to get good grades on the tests. I was looking for a smoother experience than the book, not to be told to read the book, which I can do without a course.

I would have learned more if the course had a coding assignment each week, or at least example code available for similar problems. I had a good theoretical understanding of everything we needed to do but very poor practical understanding.

The course did serve as a good introduction to the theory of reinforcement learning, and certainly acts as a good starting point.

The programming exercises are quite tough and difficult to code on our own. Concepts were explained nicely, however, lacks examples. Working out examples would have given an even better insight. Another video that could have proven useful is how to convert a real-world problem into an MDP.

Course is rather poorly structured. Some videos explain concepts better than others but come later in the courses. There's not enough of a summary of terms, and seems to follow the suggested book almost word for word. The course should use the book as supplementary not complimentary.

The good thing about this course is that it is based on the bible of reinforcement learning and it is thoughts by the experts in the field. However, the pace of the teaching is extremely fast and it is quite hard to keep with the pace even for someone with some background in the RL.

It needs more explanation about the fundamentals, examples and sections that demonstrate how each, for instance, Policy Iteration and Value Iteration differ. Despite that, the course is really good and I would recommend for a friend.

The material needs to be easier and more intuitive. Last assignment shall have some additional steps to help the student to solve it. and also to involve him more

The size of different variables has not been clearly spelled out so this makes the concept confusing and requires so much time to figure them out.

It was quite difficult for me to follow. The concepts are explained very quickly and can be though. I found exercises very helpful though.

Need a clear explanation of topics with a way to code as explained by Andrew NG in Neural networks and deep learning by deeplearning.ai

Explica las cosas muy por encima, no va al detalle, las prácticas un pelín difícil para gente que empieza.

It was quite comperhensive and intuitive one !

I automatically transferred to Auditing mode.

More explanations to theory would be nice.

More details in teaching concepts

The course was disappointing for two reasons: poor instruction and poor content. I was expecting a high quality course from Coursera, but was instead finding myself with instructors that simply read a textbook to you. The instructors did not provide any added value. They read the book, even used the exact same examples and slides as in the book. Moreover, this was done in a a boring monotone way. The instructors seemed frozen still, eyes glazed over (with boredom?) with the exception of their lips that moved as they read the slides. Good instruction includes giving more value than just reading a book: new and different examples, different explanations, or at least different wording, personal commentary, sharing own intuition, and linking material to the broader world, making connections between ideas. All of this was missing. Furthermore, the course is not inclusive. The few examples that were chosen were applications to chess and golf. In other words, activities of the privileged few. RL is highly relevant in our world where AI solutions are springing up in all areas of life. There is a wealth of examples that are relatable to a wide variety of people. Instead, by choosing golf and chess, the instructors are alienating the majority of their students. This is in stark contract to Coursera's own mission of expanding and promoting access to high quality education for ALL people regardless of their background (including socio-economic background). The course could be improved by adding content (commentary, explanations, examples, discussions) that has not appeared in the book. Relating this content in a student friendly manner (not monotonically reading slides). In short, the instructors should follow the basics of modern provably effective teaching practices.

The videos are most likely talk about the content in the book without any extra value in explanation. For me, it took a lot of effort to read the book, refers other resources to understand the content.