EU Regional School - Sra Seminar
Prof. Dr. Sra - Introduction to Machine Learning
Max Planck Institute for Intelligent Systems, Stuttgart & Carnegie Mellon University
Machine learning studies the question: "how to build (efficient) machines that can learn from examples"? Learning refers to the ability to improve performance according to some measure, as the machine gets to process more data. For example, email spam filters that learn to block unwanted email and phishing attempts, robots that learn to navigate their environments by exploring it, or more generally systems that autonomously adapt to changing environments.
This short course is targeted at students with little or no prior exposure to machine learning. We will cover some theoretical basis for the subject, along with some examples on how to apply machine learning ideas to some applications. Modern machine learning encompasses a large body of interdisciplinary knowledge, e.g., from data mining, information theory, statistics, functional analysis, computer science, optimization, and several others. Therefore, we will include some remarks on several of these connectionthe during the course. Time permitting, we will go through some exercises on a computer. Students with a background in linear algebra, statistics, and optimization will be at an advantage, though these are not hard requirements.