Course Overview
Learning Objectives
- Understand the fundamentals of machine learning algorithms and their applications in safety engineering.
- Explore how machine learning techniques can be used for risk assessment and hazard identification.
- Learn methods for collecting and preprocessing data relevant to safety engineering problems.
- Gain proficiency in applying supervised, unsupervised, and reinforcement learning algorithms to safety-related datasets.
- Understand the ethical considerations and potential biases involved in using machine learning for safety applications.
- Develop skills in evaluating the performance of machine learning models in safety contexts.
- Explore case studies and real-world examples of machine learning applications in safety engineering.
- Learn how to interpret and communicate the results of machine learning models to stakeholders in safety-critical industries.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):