Applied AI for Engineers and Scientists: Foundations

University of Glasgow via Coursera Specialization

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Overview

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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. 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

Courses

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13 hours 7 minutes
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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. In partnership with MathWorks, enrolled students have access to MATLAB for the duration of the course.
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8 hours 24 minutes
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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. In partnership with MathWorks, enrolled students have access to MATLAB for the duration of the course.
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16 hours 48 minutes
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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. In partnership with MathWorks, enrolled students have access to MATLAB for the duration of the course.