Machine Learning SPICE®
Machine Learning SPICE®
Course Duration: 1 Day
Machine learning has been used in various industries and has now become an integral part of the automotive sector. The benefits of machine learning in the automotive industry are enormous as it involves learning algorithms used in various applications including vehicle navigation, security, improving safety, reducing emissions, etc.
Considering the need for machine learning, Automotive SPICE® has included machine learning in the latest release of Automotive SPICE® v4.0.
This one-day course is provided for individuals who wish to gain an understanding of the machine learning processes included in Automotive SPICE®.
Learning Objectives
· Understand the details of the machine learning processes:
o MLE.1 – Machine Learning Requirements Analysis
o MLE.2 – Machine Learning Architecture
o MLE.3 – Machine Learning Training
o MLE.4 – Machine Learning Model
o SUP.11 – Machine Learning Data Management
· Interpret support and management processes for machine language processes
Course Outline
Day One
- Introduction and Overview
- Basics of Machine Language
- The Machine Learning Processes
- MLE.1 – Machine Learning Requirements Analysis
- MLE.2 – Machine Learning Architecture
- MLE.3 – Machine Learning Training
- MLE.4 – Machine Learning Model
- SUP.11 – Machine Learning Data Management
- Breakout Exercise
Who Should Attend
The course has been designed for those involved in project management, QA activities, design, development and production of electrical and electronic-based vehicles products – including the systems, software and mechanical engineers and managers – that have a basic understanding of process and software development who wish to use and implement machine language in their project.
Course Materials
Each participant will receive a course manual.
Note: Omnex does not provide copies of standard(s) during training courses, but clients are encouraged to have their own copy.
Pre-Requisite
- Experience in management and/or software/system development in the automotive domain