Regularization Methods for Machine Learning 2016 | Advanced Machine Learning

Comprehensive understanding of regularization methods, crucial for high-dimensional learning problems. Suitable for those interested in the latest developments in machine learning and its practical applications.

Introduction

Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. This course focuses on regularization techniques, which are key to high-dimensional learning.

Highlights

• Covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data
• Focuses on a core set of techniques, namely regularization methods
• Includes both theory classes and practical laboratory sessions

Recommendation

This is an advanced machine learning course that provides a comprehensive understanding of regularization methods, which are crucial for high-dimensional learning problems. It is suitable for those interested in the latest developments in machine learning and its practical applications, especially in areas such as computational vision.