The course is enlightening. However, it requires some sort of pre-exposure to the subject and definitely not a course for novices. The major part of the learning is achieved by reading the book. Lectures are mostly a recap of important ideas in the book and for clarification purposes.

For me, math a bit harder and more opaque than other ML courses I've taken. Even though only a few lines, final programming assignment one of more challenging ones in taking book equations to python implementation. Explanations pretty clear in videos.

Great course with interesting material and good examples. The only reason for rating 4 and not 5 is because I feel that programming assignments are a little too easy. Maybe they could benefit from letting the student implement more parts.

Clear and pleasant recorded presentations. Very good and precise reading materials. Time estimate for reading materials are super optimistic. Guest lectures are at best inspiring. No real value. They are unfocused and all over the place.

I think the course was well put together and the labs were clear. My only real complaint is that the book and tests spent a lot of time proving and manipulating equations. I am mostly interested in using the formulas and programming.

The coding assignments are a bit confusing. If you expand on coding assignment and probably provide a more step by step instruction as how the functions are being defined, or how the environments are created it would be way better.

Course is fine, but there could be more coding practices then the theoretical part. There are two coding assignments which are hard to do only with the course. The course context could be extended to include more coding practices.

The course is a very good introduction to RL but the concepts are handled a little too abstractly. However this provides an excellent fundamental for the rest of the courses. I would have liked more programming exercises.

A very useful and also rather compact course. I can recommend to anyone interested in the subject matter. I did expect a little bit more hands-on action (ie. more concrete, yet still simple examples in the coding side).

This course covers fundamentals of reinforcement learning from a book, "Reinforcement Learning: An Introduction" and that is a good thing; however, the course asks you to study by yourself by reading the book.

A really good introductory course to RL. The instructors have structured the course in the same manner as in the specified textbook (which is also great), so it's easy to follow them both at the same time.

Great material, and awesome coding exercises. Some additional information or context around a few of the problems would have been great, but nonetheless the struggle allowed me to grow in my knowledge!

The video lectures were very short and just a repetition of the book itself. After we studied the book, the lectures didn't have anything new for us. They should have been different and more hands-on.

Don't think it would be unreasonable to have more demanding coding assignments where all functions are made from scratch (though the function names and some comments might be provided as an outline.

The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.

Great course, I think theory is really well explained and book is great, but including more practice exercises is needed for this course to strengthen the learning of concepts.

The course is well developed, reading the reference book is the most important thing that you will do while taking this course. The delivery of both instructors seems robotic.

Very good an enjoyable course. It seemed like the explanations dwelled on the easier parts and skipped the parts that I would like to have seen in concrete numbers.

The instructions on how to translate equations to code could have either had examples in the presentations or in the jupyter notebooks. Overall an excellent course.

It will be good to include more detailed examples and more practice exercices in week 2 and 3. Also to repair the week 4 submission.

Although, It is a good course.

Without reading the recommended book, course material would not be sufficient. Coding exercises quite good and also quizzes' are suitable for beginner level

Good understanding of the fundamentals and aptly paced. The programming assignments were very good if there were more like that the course could get better

It brings general understanding. The main focus is reading the book. Assignments are about the introduction, help to understand, but they can be improved.

I really enjoyed the course, especially the guest segments (I got to know important people from that). Presenters did a good job but can be more relaxed.

Very good introductory course for reinforcement learning. Good coding assignment, but could add more visual representation to understand the transition.