COURSE

AIAA5075

Information Theory for Machine Learning

Information Hub / Artificial Intelligence · 3 Credits

In the era of big data and artificial intelligence, information theory has become an indispensable tool for machine learning practitioners. This course aims to bridge the gap between classical information theory and its cutting-edge applications in machine learning. Students will explore the foundations of information measures, data compression, hypothesis testing, channel coding, channel capacity, entropies, and divergences, as well as their statistical learning applications. Through guest lectures by leading experts, we will also delve into the frontiers of information theory in machine learning. By the end of this course, students will be equipped with the knowledge and skills necessary to apply information theory to develop more efficient and effective machine learning technologies.