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 03 - Linear Algebra 1 |
Taken from Prof. Ravindran's Introduction to Machine Learning course, this tutorial aims to give a refresher on the basic concepts of linear algebra that we will be making use of in the upcoming lectures. Topics covered in the first part include introductory concepts and definitions like vector spaces, subspaces, norm, column space, linear independence, rank, orthogonal matrices, quadratic forms, 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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