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This course marries data parallel programming with deep learning, and helps students to work on distributed deep learning problems with big datasets. At the end of the course students will be familiar with the main machine learning and deep learning algorithms and know how to implement them using data parallel programming platforms, such as Spark and TensorFlow, on a cluster of computers and apply them on massive datasets. This course has a system-based focus, that is, student will learn not only the theory of machine learning and deep learning, but also the practical aspects of building large scale systems that take advantage of machine learning and deep learning. The course is divided into two parts. The focus of the first part is to introduce the data parallel platforms for machine learning and deep learning, and the goal of the second part is to present the deep learning algorithms and teach students how to implement them using the introduced platforms in the first part.
You can find more information about the course here.