: Intro to Reinforcement Learning, Part 2

We're starting Unit 1 on Reinforcement Learning! This is the lecture for that series. Here, we'll cover the problem of learning through interaction, what RL is, how it's different from Supervised Learning, how RL is formalized (math), and what Markov Decision Processes are Dynamic Programming is. There is no background knowledge needed for this lecture other than simple arithmetic, college algebra, and basic probability theory. We'll be doing a short refresher on the necessary concepts if need be 🙂. Hope to see you there!

Hey SIGAI, We're beginning our Unit 2 on Advanced Topics with a lecture on evolutionary computation by Dr. Annie Wu from UCF's Computer Science Department! She'll be giving the introductory lecture she gives to her graduate students in her Evolutioanry Computation course here at UCF. There will be no slides for this lecture so if you're curious to know, come out! It is going to be a great primer for our following lecture on neuroevolution. More about Dr. Wu: Dr. Wu is the Director of the Evolutionary Computation Lab at UCF and an Associate Professor in the Department of Computer Science. Find more here: http://www.cs.ucf.edu/~aswu/