Machine Learning for Engineers

This engineering course reviews theory and applications of machine learning to engineering applications with a survey of unsupervised and supervised learning methods. This course is an introduction that focuses on providing a working vocabulary of common methods and enables students to navigate the many online resources to put the information in context. This list of resources is not meant to be exhaustive.

Introduction to Programming

• Begin Python
• CACHE Computer Programming Resources
• Computer Programming for Engineers

AutoML and Deployment

• PyCaret: Low Code Machine Learning
• AutoKeras: Automated machine learning accessible to everyone
• Lobe.AI: Easy Image Classification
• Streamlit: Deploy Apps from Python Code

Optimization Courses

• Design Optimization
• Machine Learning and Dynamic Optimization

Machine Learning Courses

• Computer Science Video Courses
• Data Science Tools
• Deep Learning at MIT
• Kaggle Learning
• Machine Learning (Stanford CS229) - YouTube Playlist
• Google Machine Learning Crash Course
• Introductory Applied Machine Learning (IAML)
• Deep Learning Course, Yann LeCun @ NYU
• Natural Language Processing, Chris Manning @ Stanford
• Learning from Data, Yaser Abu-Mostafa @ Caltech
• Full Stack Deep Learning, UC Berkeley
• Aman's AI Journal
• Machine Learning (Jay Lu, Chemical Engineering, Texas Tech)

Books and Online Articles

• Machine Learning Books
• Data Science Central
• Towards Data Science
• Neptune.ai Blog

Case Studies and Competitions

• Kaggle
• Machine Learning Research Datasets
• Scikit-learn Examples
• UCI Datasets
• Unearthed Solutions

Industrial Applications

• Applied Machine Learning

Related Topics

• Engineering-specific programming (Python, Matlab) with treatment of numerical methods.
• Machine Learning for Engineers: building mathematical models for classification and regression based on training data to make empirical predictions or decisions.
• Cybersecurity for Engineers: assessing and mitigating risks from computer-based adversarial attacks on engineered systems.
• Data Science: using scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
• Data Visualization: creating graphical representations of data to extract insights.
• Internet of Things: building cyber-physical systems that connect microcontrollers, sensors, actuators, and other embedded devices. Includes mechatronics, embedded systems, distributed systems, and networking.
• High Performance Computing: programming high-performance computers (e.g., supercomputers, cloud computing) to tackle computationally-intensive engineering problems.

Cite the Website and Course

The content of this course is freely available. If you would like to cite the course in a publication, please use the following:

  Hedengren, J.D., Machine Learning for Engineers, APMonitor Online
  Course, URL: https://apmonitor.com/pds, 2022.