Machine learning is transforming the landscape of engineering and technology by enabling data-driven decision-making, automation, and innovation. This comprehensive course provides an overview of the structure, terminology, and practical applications in engineering practice, of machine learning. This course is designed as a guide for both novice, and intermediate levels of knowledge, with a final examination primarily designed to test the skills of the novice reader. As a novice on machine learning the reader can learn about the basic structure, terminology, and practical engineering applications used in this powerful computer modeling technology. As a more advanced reader, with a deeper prerequisite knowledge of computer programming principles, and more advanced statistical mathematics knowledge, this course delves deeper into the nuts and bolts of machine learning. Skills Gained:

• Gain an understanding of the foundations of ML.
• Grasp the fundamental concepts, and key terminology associated with ML.
• Learn how to collect, clean, scale, normalize, and encode data for ML.
• Delve into linear regression, logistic regression, decision trees, random forests, clustering techniques, and more.
• Gain insights into neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications in engineering.
• Discover model selection, evaluation, cross-validation, and techniques to tackle overfitting and underfitting.
• Explore real-world applications in predictive maintenance, structural health monitoring, robotics, image and signal processing, and more.
• Understand the ethical implications of machine learning, including bias and fairness, privacy, transparency, and accountability.
• Stay ahead of the curve by examining future trends in reinforcement learning, explainable AI, quantum machine learning, and IoT integration.

 

Intended Audience:

This course is intended for any engineering professional looking to incorporate machine learning into their work, for the purpose of optimizing any number of engineering processes and systems.

Learning Objectives

At the successful conclusion of this course, you’ll be able to identify and discuss:

• Introduction to Machine Learning
• Importance of Machine Learning in Engineering
• Key Concepts and Terminology
• Foundations of Machine Learning
• Data and its Types
• Supervised, Unsupervised, and Reinforcement Learning
• Feature Engineering
• Data Preprocessing, Data Collection and Cleaning
• Data Scaling and Normalization
• Neural Networks and Deep Learning
• Model Evaluation and Validation
• Machine Learning Applications in Engineering
• Ethical Considerations in Machine Learning
• Future Trends in Machine Learning
• Applications in Various Types of Engineering Practice