Machine Learning for Engineers

Professional Development Hours (PDHs) 14

November 16 -17, 2023

Description

This course provides engineers with a comprehensive understanding of machine learning techniques and their applications in various engineering domains. Through a combination of theoretical concepts, practical hands-on exercises, and real-world case studies, participants will develop the skills necessary to effectively apply machine learning algorithms to solve engineering problems, optimize processes, and make data-driven decisions. The course will cover the fundamental principles of machine learning and the theoretical bases for how they can be applied, which includes data preparation, supervised and unsupervised learning, and feature engineering. Additional emphasis is placed on industry standard for machine learning as well as ethical considerations and challenges. All the examples and exercises will be done with RapidMiner and no programming experience is required.

Who Should Attend

The course "Machine Learning for Engineers" is designed to provide knowledge and skills in machine learning specifically tailored for engineers. While the course is primarily aimed at engineers, it can also be beneficial for individuals in related fields or those with a technical background who are interested in applying machine learning techniques to engineering problems. Some of the professionals who can benefit from attending this course include:

• Engineers (e.g., software engineers, mechanical engineers, electrical engineers, civil engineers) who want to leverage machine learning techniques to enhance their work or solve engineering problems.
• Data scientists or analysts who work in engineering domains and want to expand their knowledge of machine learning and its applications.
• Researchers and scientists in engineering fields who want to incorporate machine learning into their research projects or analyze large datasets.
• Project managers or technical leads who are involved in engineering projects that can benefit from machine learning applications and need to understand the possibilities and limitations.
• Technical professionals in industries such as manufacturing, transportation, energy, telecommunications, or any other field where machine learning can be applied to improve efficiency, optimize processes, or make predictions.

It's worth noting that the specific prerequisites for the course may vary, and some basic programming and mathematics knowledge might be required to fully grasp the concepts. However, the course is generally suitable for individuals with an engineering or technical background who are interested in learning about machine learning and its applications in engineering.

Course outline

• Fundamentals of machine learning and its applications
• CRISP-DM for machine learning
• Data access and preparation with RapidMiner
• Supervised learning: linear regression, logistic regression & general linear model, Navie Bayes, decision tree, neural nets, etc.
• Unsupervised learning: correlations, clustering, association analysis, etc.
• Feature engineering

After participating in this course, you will be able to:

• Understand the concepts and principles of machine learning from examples and case studies
• Evaluate, analyze, and prepare data for modelling
• Implement solutions to solve a range of problem by building models on a machine learning platform
• Apply the knowledge of machine learning to real-world engineering problems

Course offering format (2 Days)

The course is offered in an online (live lectures) format.