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This Specialization is designed for engineering and general science students to learn and apply AI techniques most effectively and efficiently. Unlike Specializations for computer science students, the most popular and effective AI algorithms used by engineers and scientists are carefully selected and explained in an understandable way. Particular emphasis is placed on the use of these algorithms for real-world engineering and science problems. With MATLAB toolboxes, students can bypass intricate programming and use these techniques to achieve superior results. After completing this Specialization, students can understand the concepts and operating principles of key techniques in evolutionary computation and machine learning, and apply them fluently to optimization and data analysis tasks in engineering and scientific 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. The course introduction video is here.

Syllabus

Course 1: Fundamental MATLAB Programming for AI
Course 2: Evolutionary Computation and its Applications
Course 3: Machine Learning and its Applications

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This beginner-friendly course introduces the core programming concepts used throughout the Specialization and prepares you for later study in AI-based optimization and data analysis. It aims to build the MATLAB skills you need to start working with AI and data-driven science and engineering applications. You will learn how to work with variables, matrices, scripts, loops, functions, and built-in MATLAB tools. The course also covers practical data handling techniques, including tables, cell arrays, categorical data, encoding, and data visualization. These skills are essential for preparing engineering data and building reliable computational workflows. By the end of the course, you will be able to use MATLAB with greater confidence to organize data, write basic programs, and prepare for more advanced work in AI, evolutionary computation, and machine learning. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.
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This course introduces the foundations of optimization and shows how AI can be applied to real-world science and engineering optimization problems. You will learn about evolutionary computation, a branch of AI for optimization. You will explore two widely used AI-based optimization techniques: genetic algorithms and particle swarm optimization. Along the way, you will learn how these methods work, when to use them, and how to implement them in MATLAB toolboxes to solve design and decision-making problems. The course combines core concepts with practical science and engineering case studies, helping you move from theory to application. By the end of the course, you will be able to define optimization problems and use AI methods to obtain solutions in realistic contexts. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.
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This course provides a practical introduction to machine learning techniques for data analysis in MATLAB, focusing on widely used methods for real-world technical applications. You will begin by exploring the core concepts behind machine learning, including model workflows, data preparation, and the factors that affect model performance. The course then focuses on two popular techniques—support vector machines and artificial neural networks—as well as MATLAB apps that make model building and evaluation more accessible. Using practical examples, you will prepare data, build machine learning workflows, and apply classification and regression methods to science and engineering problems. By the end of the course, you will be able to use MATLAB to develop, test, and evaluate predictive models for real-world applications. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.