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This online video lecture series gives an introduction to machine learning and pattern recognition.

This online course gives a basic introduction to the field of deep learning and to building a deep learning model using the Keras library. Some prior knowledge of Python and...

This online course gives an introduction to the fundamentals of Artificial Intelligence. It includes various topics in the field along with a discussion of foundational concepts...

This course teaches how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include...

This online course shows how to work with big data and build machine learning models at scale using Spark.

This course will introduce open source tools used for parallel, distributed and scalable machine learning. After completing this course’s hands-on projects with KNIME and...

Focusing on the basics of machine learning and embedded systems, such as smartphones, this online course will introduce learners to the “language” of TinyML.

This online course will help participants to learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems.

This online lecture series presents the theory of fundamental machine learning concepts. Topics covered in the video lectures include: Bayesian theory of optimal decisions...

This online course gives an introduction to deep learning for applications such as natural language processing, computer vision, and many others. It is designed for students...

Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in...

In this course, students will create classifiers that provide state-of-the-art performance on a variety of tasks and will become familiar with some of the most successful...

This online video lecture series gives an introduction to Reinforcement Learning.

This online lecture discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods.

In this advanced online course several quantum machine learning algorithms are presented and explained. Students will also learn how to implement them in Python.

This online course shows how to design robot vision systems that avoid collisions, safely work with humans and understand their environment.

In this course, students will learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques for solving a range of data science problems.

This online course covers advanced machine learning and signal processing concepts, including supervised and unsupervised machine learning.

In this class, students will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

This online course gives a short introduction to machine learning and data science for everyone.