Courses
Join Canvas for a free online course offered Dr. Leo Liu from the University of Virginia. Please visit the Liurg Research Group if more information is needed.
The book "AI for Engineers: Basics and Implementation" was designed for engineers who want to get a quick, immersive, and practical experience of AI. The books was organized for a 15-week semester course.
Free book, useful book, and easy book.
Learn the topic of AI engineering via a course designed for people who has an engineering background, who are intersted in AI applications in Engineering, and who want .
Courses tested at Universities and real applications.
Get to know the peers for help, collaborations and feedback, contribute to the community via data and tool sharing, and identify needs, issues, and applications.
Learn, work, and innovate as a community member.
Join Canvas for a free online course offered Dr. Leo Liu from the University of Virginia. Please visit the Liurg Research Group if more information is needed.
Join multiple events to get connected to the community for AI enginering.
You can participate in a variety of ways to both contribute to and benefit from the booming of the AI world.
Get to know what are available to boosting your learning and work for AI.
Types, history, concepts, math, and other basic knowledge that you will need to know for AI.
AI tools like Python, Numpy, Pandas, Matplotlib, Scikit-Learn, Tensorflow, Keras, OpenAI Gym,...
From basic linear models for regression and classification to more advanced kernels and regularization.
From the very basic decision trees to random forests and explore the use such algorithms in engineering.
Learn the basics of SVM and extend to more commplicated applications with Kernel methods
Check how Beyasian statistics can be applied to engineering tasks from Naive Beyasian to Beyesian Networks.
Let us learn ANN from the basics. See how to construct ANN with clear math and implementation details.
Advance from ANN to the state of the art deep neural network applications for scalable engineering applications.
Explore a variety of popular unsupervised learning techniques for clustering engineering data.
Get a quick journal to the world of dimensionality reduction to see how to better select and utilize data.
Enjoy reinforcement learning and its deep learning extensions for better control, intelligence, and resilience.
Various resources are available for AI engineering
