Avis et commentaires pour d'étudiants pour Fundamentals of Reinforcement Learning par Université de l'Alberta

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.

They put a lot of effort into it the course, however, they choose for some reason not to share the slides with their students. The accompanying book may be the standard, but yet it does not summarize the content as the slides do.

The programming examples are to simple and to few.

A vast amount of the video contains 'what we are going to cover' and 'what we have have'. This would make sense, if there are longer videos, but not if there is just one or two minutes of content.

the first and only other coursera course I took was mathematics of machine learning from imperial university of london. I found it challenging and educational, with fantastic presentation. it may serve as a good model to improve this course

A Lot of theoretical math and Too few code I recommend to show this complex mathematical equetion in code also

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