ECEA 5386 Project Planning and Machine Learning
2nd course in the Developing Industrial Internet of Things Specialization.
Instructor: David Sluiter, BSEE, Lecturer
Prior knowledge needed: ECEA 5385 Industrial IoT Markets and Security, experience with technical writing, foundational knowledge and experience in embedded systems architecture, C programming, knowledge of digital logic analyzers and protocol analyzers
Learning Outcomes
- How to staff, plan and execute a project.
- How to build a bill of materials for a product.
- How to calibrate sensors and validate sensor measurements.
- How hard drives and solid-state drives operate.
- How basic file systems operate, and types of file systems used to store big data.
- How machine learning algorithms work - a basic introduction.
- Why we want to study big data and how to prepare data for machine learning algorithms.
Syllabus
Duration: 10 hours
In this module, the instructor shares with you his experience in product planning, staffing, and execution. You will perform a product tear down and build a bill of materials (BOM) for that product.
Duration: 2 hours
In this module, you will learn about sensors, and in this case, a temperature sensor. You will learn how to calibrate and then validate that a temperature sensor is producing accurate results. We will study how data is stored on hard drives and solid-state drives. We will take a brief look at file systems used to store large data sets.
Duration: 3 hours
In this module, we look at machine learning, what it is, and how it works. We take a look at a couple of supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required of you. Instead, working source code is provided to you so you can play around with these algorithms. We wrap up by providing some examples of how ML can be used in the IIoT space.
Duration: 3 hours
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 an ML exercise.
Duration: 1 hour
The final exam for this module is based on the lecture content only.
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Grading
Assignment | Percentage of Grade |
Quiz 1 | 10% |
Quiz 2 | 10% |
Quiz 3 | 10% |
Quiz 4 | 10% |
Product tear down, build a bill of materials (BOM) | 20% |
Final Exam | 40% |
Letter Grade Rubric
Letter Grade | Minimum Percentage |
A | 94% |
A- | 90% |
B+ | 87% |
B | 84% |
B- | 80% |
C+ | 77% |
C | 74% |
C- | 70% |
F | 0% |