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.
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Machine Learning and its Applications
Ce cours fait partie de Spécialisation Applied AI for Engineers and Scientists: Foundations
Instructeur : Bo Liu
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Ce que vous apprendrez
Learn essential concepts and working principles of machine learning algorithms and their application in science and engineering.
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Il y a 5 modules dans ce cours
One of the most important applications of AI in science and engineering is classification and regression using machine learning. This module introduces essential concepts and principles in machine learning using two simple but useful machine learning techniques. After learning this module, students will be able to:
Inclus
10 vidéos7 lectures1 devoir2 éléments d'application1 sujet de discussion
Continuing the last module, this module still introduces essential concepts and principles in machine learning with a focus on model training and evaluation. After learning this module, students will be able to:
Inclus
7 vidéos4 lectures1 devoir2 éléments d'application1 sujet de discussion
This module introduces fundamental data preparation concepts and techniques to improve data quality in order to promote machine learning models providing good outcomes in real-world science and engineering practice. After learning this module, students will be able to:
Inclus
8 vidéos6 lectures1 devoir3 éléments d'application1 sujet de discussion
This module introduces support vector machines (SVMs), which is one of the most effective and popular methods for classification. After learning this module, students will be able to:
Inclus
12 vidéos4 lectures1 devoir2 éléments d'application
This module introduces artificial neural networks (ANNs), which is one of the most effective and popular methods for regression and classification. After learning this module, students will be able to:
Inclus
14 vidéos5 lectures1 devoir1 élément d'application
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