Computers have been the workhorses of modern society in every aspect. And mechanical engineers always use computer to do many kinds of computational work including control, robotics, fluid mechanics, heat transfer, ...etc. However, with the ever-changing technology, there are more and more numerical methods and algorithms been developed, and even a new type of computer structure is invented - quantum computer. Therefore, this course aims to equip our students to better understand these new tools and to face the coming challenges in the future. This course will introduce two most advanced topics in the computational field, namely, machine learning and quantum computation. 
 
Machining learning and artificial intelligence play more and more important roles in current engineering disciplines. This course will introduce the basics of machine learning and explore how such advanced techniques can be applied in the mechanical engineering field. Students will learn the art and science of Machine Learning from the fundamentals to state-of-the-art models. A strong emphasis is put on the principles of problem solving, and how machine learning techniques can be used to tackle practical engineering problems. The students will complete the course with the confidence to explore these topics further and apply them to other areas of interest themselves. 

Students should have linear algebra knowledge and some programming background to understand the course content. We will use Matlab/Python as a medium to implement the machine learning models.

Quantum computer can perform computations much faster than classical computer on certain type of problems, which starts a new page in computation history. Many problems that are intractable on classical computers may be tractable with the aid of quantum computing. This course will introduce different quantum computer hardware designs and mainly focus on quantum computing algorithms. We will start from the basic knowledge of qubits to fundamental quantum algorithms such as quantum Fourier transform, Shor's algorithm, Grover's algorithm...etc. Recent developed algorithms will be introduced as well, such as quantum machine learning, imaginary time control, quantum chemistry applications...etc. Especially quantum machine learning as a new rising topic will serve as connecting bridge between classical machine learning and quantum computing. With these new tools and knowledge, quantum computers will become a powerful tool for our students to face the rapid changing challenges in this whole new era. 

For a student to pass the course, at least 30% of the maximum mark for both coursework and examination should be obtained.