One of the most important applications of AI in engineering is optimization. Optimization is almost needed everywhere in science and engineering. Compared with traditional mathematical optimization techniques, evolutionary computation, which is a branch of AI, is attracting much attention. After taking this course, students will be able to understand how evolutionary computation works and fluently use AI-based optimization techniques to solve engineering optimization problems via MATLAB. This course introduces fundamental concepts in optimization and the working principles of genetic algorithm and particle swarm optimization in a comprehensive and understandable way. Case studies from real-world engineering are provided, making sure students have the ability to apply what they have learned in real practice.

One of the most important applications of AI in engineering is classification and regression using machine learning. After taking this course, students will have a clear understanding of essential concepts in machine learning, and be able to fluently use popular machine learning techniques in science and engineering problems via MATLAB. Among the many machine learning methods, only those with the best performance and are widely used in science and engineering are carefully selected and taught. To avoid students getting lost in details, in contrast to teaching machine learning methods one by one, the first two lectures display the global picture of machine learning, making students clearly understand essential concepts and the working principle of machine learning. Data preparation is then introduced, followed by two popular machine learning methods, support vector machines and artificial neural networks. Practical cases in science and engineering are provided, making sure students have the ability to apply what they have learned in real practice. In addition, MATLAB classification and regression apps, which allow easy access to many machine learning methods, are introduced.