Course Description
Exposes students to and trains them for the digitally transforming industrial world. This includes application of digital technologies ranging from cloud computing to AI techniques to predictive analytics. The theory and practical aspects of Industrial Internet of Things will also be covered.
Additional Requirements for Graduate Students:
Graduate students will also perform an additional research topic
survey of predictive analytics and edge computing space.
Athena Title
Industrial Internet of Things
Prerequisite
ELEE 2040 or ENGR 1140 or CSCI 1301-1301L
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
Upon successful completion of this course, students will be able to: • Understand the concepts of the Industrial Internet of Things and how it improves industrial productivity with digital technologies. • Learn the techniques of AI and machine learning and how they are applied in the industrial world in the context of predictive analytics. • Become familiar with cloud computing and develop scaleable applications in that environment. • Learn about and build end-to-end solutions starting from connecting devices to an IIoT platform to building apps to manage their performance.
Topical Outline
Introduction to IIoT and its Applications a. What is IoT and IIoT? b. Examples of IIoT applications c. What does IIoT mean to industrial productivity? d. IoT enabling platform and its architecture Data Types and Sensors a. Data sources and data types b. Different types of industrial sensors c. Commonly measured quantities in industry Basics of Connecting Devices a. What is a device? b. What is device connectivity? c. Communication and data transfer d. Standard communication protocols e. Built in connectivity and software agents f. Cloud-to-cloud connectivity Basics of Cloud Computing a. What is cloud-to-cloud computing? b. What is device connectivity? c. Communication and data transfer d. Standard communication interfaces e. Built-in connectivity and custom software agents f. Cloud-to-cloud connectivity Machine Learning, Data Analytics, and Visualization a. Introduction to machine learning b. Machine learning algorithms and their applications c. Model building and prediction process – data cleaning to testing d. Deep dive into common regression and classification algorithms e. Data science using Python f. Examples and exercises in data analytics g. Visualization, visual analytics, and tools Introduction to APIs and App Development a. Application programming interfaces (APIs) b. APIs at work – an important cog in the application development world c. Data format - JSON d. Examples of app development with APIs Research element a. Survey of research topics in the predictive analytics and edge computing space b. Basic research on a selected topic in the area
Syllabus