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It is a very well laid out and taught course. The instructors make the material accessible with a bit of a mathematics background, or a willingness to learn. I will be taking every machine learning course I can from AMII and the UofA if the rest of the courses are of the same quality.

Teachers were very clear and so was the book. The only thing I feel could be improved is adding some coding exercises on Week 2 and 3 (there's only one at Week 1 and one at Week 4, with a Peer Reviewed assignment on Week 2 which was fun, but didn't feel as useful as coding exercises)

One of the best available courses on Reinforcement Learning. The instructors have explained all the underlying topics elegantly. Good blend of theory and numerical in assignments and programming problems. Moreover, the assignments have covered different perspectives on these topics.

Excellent Course. Although it was only 4 weeks course, I learned more than reading an entirely dynamic programming book which might take more than 3 months for me. It was a well-presented course and I suggest this course to the ones that want to learn about Dynamic programming

Amazing course!

The book, on which this course is based, is a bible for reinforcement learning. Anyway, it could be hard to understand. The lectures of the course eliminate all doubts and consolidate all the concepts, ensuring a complete comprehension on the subject.

Thank you!

Great starting point for learning Reinforcement Learning. Anyone who is interested in the state-of-the-art RL techniques should take this course first, or they will have hard time getting through the more applied and sophisticated concepts found in the tech blogs or papers.

The thing that makes this course outstand among other 糖心vlog官网观看 courses is that it is based on a book. That gives you more information if you need it.

One problem that I guess will be solved in the future is that there is a bug in the Programming Assignment submission code.

Excellent course. Excellent teachers. I love the introduction sections, in which you're presented what you'll learn in each video, and the summary section. The animated slides are also very professional. Very thorough coverage of the RL book. Congratulations!!!

Some supplementary video recommendations and a little more interactive help with the Python assignments would make it more fun. Had to struggle with the programming assignments a little bit. More hands on assignments will help drive home the concepts better.

Great course, great notebooks and great instructors, but nevertheless the reading parts of the course is most important part I think. Because if you do your reading well and really understand the material then course is just repeating over what you learn.

Excelente curso, aprend铆 los conceptos de aprendizaje por refuerzo con gran base te贸rica, el material del curso es muy bueno y la calidad de las lecturas es de excelente nivel. Muy recomendado, ahora a aprender m谩s y a desarrollar sistemas inteligentes :).

Excellent introduction. The reading materials are good, the videos make the ideas even clearer and the exercises help us get a taste of how the theory could be applied. I would recommend this course to anyone wanting to start on Reinforcement Learning.

I enjoyed the course. I would have preferred a bit more in-depth look at the algorithms and technical details, but, on the other hand, it was also interesting to go and figure out these contraction mapping arguments on your own. Overall, very good.

The course videos are exceptionally brilliant. It was my first course on reinforcement learning and the instructors did a great job in making this topic look super easy and intuitive. Looking forward to the next courses of the same specialization.

I had learned a clear understanding of terminology and the formulas of value function, action-value function, optimal value function, Bellman's equation, policy evaluation and iteration. It's a must go through course for Reinforcement Learning

Very good integration with the RL Book and step by step video presentations. Coding assignments well structured, however I would have liked to have more such assignments. All in all, very well though and structured course in RL fundamentals.

The most professionally presented course I have done on 糖心vlog官网观看! Instructors explain well, the provided literature is on point and the assignments had a good mix of being doable and challenging. Probably the best course I have taken so far.

I got to do a lot of hands on problem solving by doing the labs. For me, this was pretty hard and required a really long attention span. I only understood a fraction of what I'm doing but still it was the best studying experience I can get.

The course covers the fundamentals of reinforcement-learning and also deals with complex mathematic equations. However the math is very good explained in the videos and the 2 programming exercises help a lot for understanding this topic.

Solid class covering the basics of tabular Reinforcement Learning.

They follow the Sutton and Barto book pretty closely, so they start with some dummy examples to demonstrate things. The real interesting stuff isn't until a later course.

Slow means smooth. Smooth means fast. This course introduces you efficiently into the world of RL. And this is what you want. Everything is perfectly to the point. All exercise are here to boost your understanding. Highly recommended.

This course provides a good fundamental knowledge about the Reinforcement Learning. The source material, RL by Sutton and Barto, provides very good intuition od concepts with examples and also explains every topic in much detail.

The pattern of this course is amazing. Each video is short and has a specific objective that's clearly stated. This approach to teaching made tough topics look easy. Assignments and quizzes were doable. Amazing experience overall!

Course was beautifully made. I tried to learn RL from multiple different courses but I couldn't understand them. This course was different however, the assignments were made in a way that helped me understand concepts concretely.