Course Title: Machine Learning in Engineering
Course Description:
This course introduces students to the application of machine learning techniques in engineering disciplines. It covers the fundamental concepts and algorithms of machine learning and how they can be applied to solve engineering problems. Topics include supervised and unsupervised learning, neural networks, support vector machines, decision trees, and deep learning. Students will gain hands-on experience using machine learning tools to analyze engineering data, optimize processes, and develop predictive models. Case studies from fields such as mechanical, electrical, civil, and chemical engineering will demonstrate the real-world impact of machine learning.
Course Topics Include:
- Introduction to machine learning and its relevance to engineering
- Supervised learning techniques: regression, classification
- Unsupervised learning: clustering, dimensionality reduction
- Neural networks and deep learning in engineering applications
- Support vector machines and decision trees
- Model evaluation and performance metrics
- Optimization techniques and their applications in engineering
- Case studies in mechanical, electrical, civil, and chemical engineering
Course Objectives:
- Understand the basic concepts of machine learning and its algorithms.
- Apply machine learning techniques to solve real-world engineering problems.
- Develop predictive models for system optimization and process improvement.
- Analyze and interpret engineering data using machine learning tools.
- Prepare students for advanced studies or careers in machine learning and engineering.
Prerequisites:
- Basic knowledge of programming (Python recommended).
- Understanding of statistics and linear algebra is beneficial.
Credit Hours:
3 (with optional lab or project component).
Assessment Methods:
- Quizzes and exams
- Homework assignments and projects
- Hands-on machine learning exercises
- Class participation and discussions
Ideal for:
Students interested in the intersection of machine learning and engineering, particularly those pursuing careers in data science, automation, or system optimization.