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November 29, 2023
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Arm Scientists and Engineers with AI Techniques. Understand and employ AI techniques for engineering/scientific optimization and machine learning bypassing intricate programming.
Instructor: Bo Liu
Included with
Recommended experience
Beginner level
This specialization targets general science and engineering BSc./BEng/MSc./MEng students, who want to learn and apply AI techniques.
Recommended experience
Beginner level
This specialization targets general science and engineering BSc./BEng/MSc./MEng students, who want to learn and apply AI techniques.
Concepts and principles of machine learning, evolutionary computation and their real-world applications via MATLAB.
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January 2025
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This specialization is designed for engineering and general science students to learn and apply AI techniques most effectively and efficiently. Different from specializations for computer science students, the most popular and effective AI algorithms that are used by engineers and scientists are carefully selected and explained understandably. A particular emphasis is the use of these algorithms for real-world engineering and science problems. Through MATLAB toolboxes, students can bypass intricate programming to use these techniques and achieve superior results. After taking this specialization, the students can understand the concepts and working principles of key techniques in evolutionary computation and machine learning, and use them fluently in optimization and data analysis tasks in engineering and science practice.
This specialization is the result of the research program of AI for science and engineering by University of Glasgow (an international top 100 university), investigating the effective and efficient way of teaching AI for non-computer science/mathematics-focused students.
Applied Learning Project
The assignments ask students to solve (simplified) real-world scientific and engineering problems, including optimization and machine learning, using AI techniques. The learners will analyze the problems and apply the AI knowledge that was taught with the support of the MATLAB platform and toolboxes.
This course introduces the fundamentals of the programming platform of this course, MATLAB. Through MATLAB’s toolboxes, engineers can make use of AI techniques bypassing intricate programming and achieve superior results. After learning this course (3 modules), students will be ready to learn AI techniques using MATLAB in terms of programming skills.
In this course, MATLAB fundamentals, particularly those that are useful for applying AI techniques using MATLAB, are introduced. This includes manipulating variables and matrices in MATLAB, MATLAB scripts, graphs, using built-in functions, defining and using custom functions, conditionals and program control, loops, table arrays and cell arrays to manipulate data, categorical data and one-hot encoding of them, etc. Case studies will be provided for writing objective functions in engineering optimization and data cleaning for building machine learning models, which are the fundamentals of Courses 2 and 3.
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.
The University of Glasgow has been changing the world since 1451. It is a world top 100 university (THE, QS) with one of the largest research bases in the UK. We are a member of the prestigious Russell Group of leading UK Universities with annual research income of more than £179m. The University’s #TeamUofG community is truly international with over 8000 staff and 28,0000 students from more than 140 countries. A 2019 Time Out survey placed Glasgow in the top ten cities in the world. Ranked between Berlin and Paris, Glasgow was voted number one for both friendliness and affordability. Right now our dedicated community of staff, students and alumni is working to address the challenges of Covid-19 and understand how we can make life safer for everyone.
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11 weeks
Fundamental programming, and fundamental mathematics for 1st year university students.
Yes. The 3 courses are sequential and need to be taken one by one.
Only to University of Glasgow students.
(1) Understand the fundamental concepts of AI. (2) Understand working principles of key techniques in evolutionary computation and machine learning, including genetic algorithm, particle swarm optimization, linear regression, k-nearest neighbors, support vector machines, and artificial neural networks, etc. (3) Be able to use the key AI techniques in science and engineering practice.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.