Reinforcement Learning
Reinforcement Learning. Instructor: Prof. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras. Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI. The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research. (from nptel.ac.in)
| Lecture 01 - Probability Basics 1 |
Taken from Prof. Ravindran's Introduction to Machine Learning course, this tutorial aims to give a refresher on the basic concepts of probability that we will be making use of in the upcoming lectures. Topics covered in the first part include introductory concepts such as sample space, events, probability measure, conditional probability, Bayes' rule, etc. You are encouraged to go through additional resources for any topic covered here that you don't feel comfortable with, since the aim of this tutorial is to recap important topics and not go into any concept in great detail.
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