AI Learning for Engineers

AI Engineering

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Read the Book

The book "AI for Engineers: Basics and Implementation" is 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.

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Lecture Series

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 .

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More Resources

Get to know peers for help, collaborations and feedback via data and tool sharing and interactive courses on Canvas and Course (to be available soon).

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Courses & Specializations

Join Canvas for a free online course offered Dr. Leo Liu from the University of Virginia (link to be added soon). Please visit the Liurg Research Group if more information is needed.

Videos & AI 101

Enjoy additional multimedia for learning AI, AI in Engineering, and AI in life. You can visit a YouTube channel developed for sharing AI learning materials. More channels for sharing and communicating AI news, trends, and events will be created.

Tools & Problems

Get to know what are available to boosting your learning and work for AI. Code is frequently shared on GitHub.

Learn as needed, from book, code/datasets, specific lectures, to college courses and pro certificates.

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AI Engineering Book

Course #1

Basics of AI

Types, history, concepts, math, and other basic knowledge that you will need to know for AI.

Author 1
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Course #2

Tools for AI

AI tools like Python, Numpy, Pandas, Matplotlib, Scikit-Learn, Tensorflow, Keras, OpenAI Gym,...

Author 2
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Course #3

Linear Models

From basic linear models for regression and classification to more advanced kernels and regularization.

Author 3
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Course #4

Decision Trees

From the very basic decision trees to random forests and explore the use such algorithms in engineering.

Author 4
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SVM

Learn the basics of SVM and extend to more commplicated applications with Kernel methods

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Bayesian Methods

Check how Beyasian statistics can be applied to engineering tasks from Naive Beyasian to Beyesian Networks.

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Neural Network

Let us learn ANN from the basics. See how to construct ANN with clear math and implementation details.

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Deep Learning

Advance from ANN to the state of the art deep neural network applications for scalable engineering applications.

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Clustering

Explore a variety of popular unsupervised learning techniques for clustering engineering data.

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Dimension Reduction

Get a quick journal to the world of dimensionality reduction to see how to better select and utilize data.

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RL

Enjoy reinforcement learning and its deep learning extensions for better control, intelligence, and resilience.

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Enjoy and Build the AI World

Explore the popular resources for AI Engineering

Various resources are available for AI engineering

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