Project Planning and Machine Learning

Overview

Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder

Syllabus

Project Planning and Staffing

In this module I share with you my experience in product planning, staffing and execution. You will learn about the importance of first defining requirements, second performing the design work first, and then lastly writing the code.

Sensors and File Systems

In this module you will learn about sensors, and specifically a temperature sensor. You will learn how to calibrate a temperature sensor system, which helps you measure and improve the accuracy of that system. You will learn how data is stored on hard drives and solid state drives. You will learn about 3 file systems that are used to store large data sets, which are often referred to as "Big Data".

Machine Learning

In this module we look at machine learning (ML), what it is and how it works. You will learn about several supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required of you. Instead I provide working source code to you so you can play around with these algorithms. I wrap up by providing some examples of how ML can be used in the Industrial IoT space.

Big Data Analytics

In this module you will learn about big data and why we want to study it. You will learn about issues that can arise with a data set and the importance of properly preparing data prior to a Machine Learning exercise.