This course is an advanced graduate course in cloud computing and machine learning. This course exposes students to various cloud computing models and introduces them to performing machine learning on the cloud. The course material introduces students to various cloud providers including Amazon AWS, Google GCE, and IBM Cloud and their machine learning service capabilities. Students will learn how to build cloud systems for machine learning, the application characteristics, and develop hands-on experience with programming machine learning applications on these cloud platforms.
The course does not follow a specific textbook.
Reference book: Cloud Computing for Machine Learning and Cognitive Applicaitons ISBN: 9780262036412.
Research papers and other important material on relevant topics will be made available during the course.
Introduction to cloud computing
Introduction to machine learning on the cloud: Domains, Frameworks, Use cases
Getting started with machine learning on the cloud
Compute, network, storage infrastructure organization in the clouds
Distributed deep learning
Disaggregrated systems and computing
Understanding of important algorithms such as sorting, searching, graphs, etc.
Understanding of the design, use, and implementation of imperative, object-oriented, and functional programming languages.
Understanding of Computer Architecture, C/C++ programming, OS design, process, stack/heap, threads, file-system, IO, Networks.
Intermediate programming skills.
There are no exams for this class.
Grades are based on the project work, class presentation, and class participation.
Project 1: 10%
Project 2: 15%
Project 3: 30%
There will be at least 2 class presentations per student.
This is a graduate level class, so we are looking for your active participation in the class.
Your attendance alone does not qualify as active participation.