Learner Reviews & Feedback for Prediction and Control with Function Approximation by University of Alberta
About the Course
Top reviews
AC
Dec 1, 2019
Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.
SJ
Jun 24, 2020
Surely a level-up from the previous courses. This course adds to and extends what has been learned in courses 1 & 2 to a greater sphere of real-world problems. Great job Prof. Adam and Martha!
76 - 100 of 133 Reviews for Prediction and Control with Function Approximation
By Ola D
•Jun 15, 2022
Fantastic course with fantastic instructors
By İbrahim Y
•Oct 5, 2020
the course is the intro for high level RL
By MJ A
•Jan 23, 2021
perfect and thank you for this course
By Teresa Y B
•May 11, 2020
Very Useful and Highly Recommend !!!
By Stewart A
•Oct 31, 2019
Simply the best course on this topic.
By Farzad E b
•Aug 4, 2022
It was perfect, I really enjoyed it
By Junchao
•May 29, 2020
Very good and self-oriented course!
By Fernando A S G
•Mar 26, 2021
Excellent course! Thanks a lot!
By Wei J
•Oct 11, 2020
It is a very perfect RL course.
By Antonis S
•May 30, 2020
Really a well-prepared course!
By Ignacio O
•Nov 29, 2019
Really good, I learned a lot.
By FREDERIC N
•May 2, 2020
Great speakers and content!
By Majd W
•Feb 1, 2020
Very practical course.
By 李谨杰
•Jun 17, 2020
Excellent class !!!
By Mohamed A
•Sep 11, 2021
very good course
By Hugo T K
•Aug 18, 2020
Excellent course.
By Murtaza K B
•Apr 25, 2020
Excellent course
By Ivan M
•Aug 30, 2020
Just brilliant
By Juan “ L
•Aug 3, 2022
great course!
By Oriol A L
•Nov 19, 2020
Very good!
By Cheuk L Y
•Jul 8, 2020
Very good!
By Jialong F
•Feb 23, 2021
gooood!
By Justin O
•May 18, 2021
Great
By Artod
•Feb 27, 2021
Super
By Ananthapadmanaban, J
•Jul 19, 2020
I am disappointed with policy gradients being introduced on last week of the 3rd course. The instructors need to understand that 12 weeks is too much for introduction before starting a good project to implement the concepts with a hope to better understand them (course 4). Policy gradients should have been introduced in week 3/4 of course 2 itself. The content before that should be made more efficient (4 weeks to understand until q-learning/sarsa and 2 weeks to understand function approximation should be enough). I realized after course 2 that Andrew Ng has 3/4 videos on RL in the recently released ML class from Stanford. I am yet to go through them, but I feel they may explain these faster with same amount of rigour. However, the stanford class assignments are not public, which makes this course still useful because of the assignments. However, thanks to the instructors for this course.
Coursera Footer
