Machine Learning and Artificial Intelligence

Send feedback about this page to Dr. John Hedengren (Brigham Young University), Dr. Martha Grover (Georgia Institute of Technology), or Dr. Victor Zavala (University of Wisconsin-Madison).

Machine Learning & AI Teaching Resources for Faculty and Students

The following page provides a curated selection of teaching resources for faculty and students involved in machine learning (ML) and artificial intelligence (AI) courses, with an emphasis on their applications in engineering. These resources include syllabi, course materials, online courses, textbooks, datasets, tutorials, and case studies relevant to the use of ML and AI in various fields of engineering. The aim is to equip both educators and learners with the tools they need to integrate ML/AI concepts effectively into their curriculum and research.

Courses & Course Materials

These links include comprehensive courses, video lectures, and textbooks to guide both students and faculty in their journey to understand machine learning and artificial intelligence.

• Machine Learning for Engineers
  A selection of engineering-specific courses that cover the basics of ML and its applications in real-world engineering problems.
• Machine Learning (Stanford CS229)
  A complete YouTube playlist of lectures from Stanford’s CS229 course, covering a range of machine learning techniques.
• Google Machine Learning Crash Course
  A hands-on introduction to machine learning that includes exercises and interactive learning.
• Introductory Applied Machine Learning (IAML)
  A comprehensive course designed for beginners, with applications of ML in various engineering contexts.
• Deep Learning Course by Yann LeCun @ NYU
  A course that delves into deep learning, taught by one of the foremost experts in the field.
• Learning from Data (Yaser Abu-Mostafa @ Caltech)
  A detailed lecture series that focuses on supervised learning and the foundations of machine learning.
• Full Stack Deep Learning (UC Berkeley)
  A full-stack course that covers the process of building, training, and deploying deep learning models.

Books and Articles

For those interested in expanding their knowledge of ML and AI, these books and articles offer deep insights and practical applications.

• Bishop & Bishop Deep Learning Book
  A highly regarded textbook that offers an introduction to deep learning concepts, theories, and methodologies.
• Deep Learning Gitbook on Materials and Chemicals
  An open-source guide that applies deep learning techniques to the materials and chemicals industry.
• Towards Data Science Blog
  A well-known resource for articles, tutorials, and discussions about ML and data science applications.
• Data Science Central
  A community and resource hub for professionals in the data science field, covering trends, methods, and technologies in ML.

Datasets & Case Studies

Machine learning thrives on real-world data, and these resources provide access to curated datasets and industry case studies.

• Kaggle
  A platform with numerous ML datasets and competitions. Great for students and researchers to test their skills on real-world problems.
• Dow Data Challenge
  This challenge provides a dataset for developing ML models that address real-world issues in chemical manufacturing.
• UCI Datasets
  A comprehensive collection of datasets for machine learning research, including multiple areas of engineering and applied sciences.
• ML Code Challenges
  A series of challenges to test and hone coding skills in ML and AI applications, ideal for students and professionals alike.

Tools and Tutorials

To assist with the practical application of ML and AI concepts, these tools and tutorials offer resources for students to practice and gain hands-on experience.

• Machine Learning in Chemical Engineering: Python Tutorials
  Learn how to implement machine learning techniques using Python with case studies in chemical engineering applications.
• PyCaret: Low Code Machine Learning
  A framework that simplifies the process of developing machine learning models, making it accessible even for beginners.
• AutoML and Deployment Tools: AutoKeras, Lobe.AI
  These tools simplify the process of automating machine learning workflows, from model development to deployment.
• Streamlit: Deploy Apps from Python Code
  A resource that guides users in deploying ML models as web apps using simple Python code.

Contribute Additional Resources

To ensure this page remains current and comprehensive, we encourage students, faculty, and professionals to share their suggestions, corrections, and additional resources. Please take a moment to complete our online survey to contribute valuable teaching materials and resources. Your input will help us improve the offerings and ensure that the latest advancements in ML/AI education are shared widely.

Contribute Resources

Send feedback about this page to Dr. John Hedengren (Brigham Young University), Dr. Martha Grover (Georgia Institute of Technology), or Dr. Victor Zavala (University of Wisconsin-Madison).