I am studying about sequential decision making and I am willing to know if there is any course which is recorded and is publically available covering topics in dynamic programming (DP), reinforcement learning (RL), bandit problem, approximate DP\RL, online optimization?
There are a few courses on Coursera that offer such learning materials.
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
- Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
The previous answer provided a great list on the classical dynamic programming. For Reinforcement learning and Deep Reinforcement learning, a wonderful online free course on RL by David Silver (the first author of the AlphaZero, AlphaGo algorithms) exists: YouTube links
A more advanced course by Sergey Levine exists for free at: YouTube link
For both course the link to the material of the course is available too.