The course will present and contrast the statistical, computational and game-theoretic models for learning.

This course focuses on developing a theoretical understanding of the statistical properties of learning algorithms. Topics: Generalization bounds via ...

This course focuses on the theoretical foundation of machine learning. The first half of the course is devoted to study fundamental learning problems.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning ...

The course will present and contrast the statistical, computational and online models for learning. We will present and rigorously analyze some of the most ...

This course is an advanced graduate-level/seminar course on some topics in theoretical machine learning. The course is ideal for graduate students and senior ...

This course introduces various machine learning algorithms with emphasis on their derivation and underlying mathematical theory.

Course Outline. This is course was offered in the online mode. The course is divided into 3 modules. (In an in-person semester more modules will be covered.).

This course focuses on understanding the mathematical theory (but not necessarily the modern deep learning theory) behind machine learning algorithms.

In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms ...