Machine Learning and its Applications

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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

Inclus avec

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

47 avis

niveau Débutant

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

47 avis

niveau Débutant

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Learn essential concepts and working principles of machine learning algorithms and their application in science and engineering.

Compétences que vous acquerrez

Catégorie : Model Evaluation
Catégorie : Data Preprocessing
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)

Outils que vous découvrirez

Catégorie : Classification Algorithms

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

5 devoirs

Enseigné en Anglais

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Ce cours fait partie de la Spécialisation "Applied AI for Engineers and Scientists: Foundations"

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Il y a 5 modules dans ce cours

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.

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

10 vidéos Total 65 minutes

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations 6 minutes
Module 7: Introduction to Week 7's Study 2 minutes
Machine Learning Fundamentals: What is Machine Learning 6 minutes
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (1) 6 minutes
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (2) 9 minutes
Mapping Inputs to Outputs: Data Representation 11 minutes
Mapping Inputs to Outputs: Parametric ML Model 8 minutes
Mapping Inputs to Outputs: Non-Parametric ML Model 8 minutes
Mapping Inputs to Outputs: Evaluate Output 3 minutes
MATLAB Implementation: Simple Linear Regression and KNN 7 minutes

7 lectures Total 56 minutes

Specialization and Course Structure 10 minutes
Specialization Sample Certificate 10 minutes
How to Access MATLAB Online 5 minutes
Materials for Machine Learning Fundamentals 10 minutes
Materials for Mapping Inputs to Outputs 10 minutes
Materials about MATLAB Implementation 10 minutes
CSV File for Module 7 Assignment 1 minute

1 devoir Total 30 minutes

Module 7 Quiz 30 minutes

2 éléments d'application Total 60 minutes

Module 7 Assignment 1 30 minutes
Module 7 Assignment 2 30 minutes

1 sujet de discussion Total 10 minutes

Module 7 Discussion on Machine Learning 10 minutes

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

7 vidéos Total 50 minutes

Module 8: Introduction to Week 8's Study 2 minutes
ML Model Training Fundamentals: Parameters, Hyperparameters, and Loss Functions 9 minutes
ML Model Training Fundamentals: Loss Functions and Gradient Descent 6 minutes
ML Model Training Fundamentals: Gradient Descent 10 minutes
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation 14 minutes
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation 5 minutes
Summary of the ML Process 5 minutes

4 lectures Total 31 minutes

Materials for ML model training 10 minutes
Materials on ML Model Evaluation Fundamentals 10 minutes
Materials for ML Process 10 minutes
CSV file for Module 8 Assignment 1 minute

1 devoir Total 30 minutes

Module 8 Quiz 30 minutes

2 éléments d'application Total 60 minutes

Module 8 Assignment 1 30 minutes
Module 8 Assignment 2 30 minutes

1 sujet de discussion Total 10 minutes

Module 8 Discussion on ML Model Quality 10 minutes

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

8 vidéos Total 38 minutes

Module 9: Introduction to Week 9's Study 2 minutes
Basic Data Cleaning Review 1 minute
Distributions, Outliers and Their Removal: Gaussian Distribution, Skewness and Outliers 7 minutes
Distributions, Outliers and Their Removal: The Z-Score and IQR Method for Outliers Removal 6 minutes
Data Transform: Normalization, Standardization, Power Transform 8 minutes
Construct Training and Test Sets for Model Evaluation: Methods, Implementation, and Stratified Sampling 6 minutes
Construct Training and Test Sets for Model Evaluation: Cross-Validation 6 minutes
Data Preparation Overview 4 minutes

6 lectures Total 51 minutes

Materials on Basic Data Cleaning 10 minutes
Materials on Distributions, Outliers and Their Removal 10 minutes
Materials on Data Transform 10 minutes
Materials on Training/Test Sets Generation 10 minutes
Materials on Data Preparation 10 minutes
XLS file for Module 9 Assignment 1 minute

1 devoir Total 30 minutes

Module 9 Quiz 30 minutes

3 éléments d'application Total 90 minutes

Module 9 Assignment 1 30 minutes
Module 9 Assignment 2 30 minutes
Module 9 Assignment 3 30 minutes

1 sujet de discussion Total 10 minutes

Module 9 Discussion on Data Preparation 10 minutes

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

12 vidéos Total 80 minutes

Module 10: Introduction to Week 10's Study 2 minutes
Support Vector Machine Fundamentals: Concepts 12 minutes
Support Vector Machine Fundamentals: Types of SVM 4 minutes
Support Vector Machines: Linear SVM of Hard Margin Classifier 7 minutes
Support Vector Machines: Linear SVM of Soft Margin Classifier 11 minutes
Support Vector Machines: Non-Linear SVM 13 minutes
Support Vector Machines: Multi-Class SVM 8 minutes
Support Vector Machine Implementation: MATLAB Implementation of SVM 5 minutes
Support Vector Machine Implementation: Iris Flower Example 6 minutes
Support Vector Machine Implementation: 2D Point Classification Example 1 minute
Case Study 1: Banknote Classification (Linear SVM) 4 minutes
Case Study 2: Raisin Classification (Non-Linear SVM) 8 minutes

4 lectures Total 31 minutes

Materials on Support Vector Machine Fundamentals 10 minutes
Materials on Support Vector Machines 10 minutes
Materials on Support Vector Machine Implementation and Case Studies 10 minutes
CSV file for Module 10 Assignment 1 minute

1 devoir Total 30 minutes

Module 10 Quiz 30 minutes

2 éléments d'application Total 60 minutes

Module 10 Assignment 1 30 minutes
Module 10 Assignment 2 30 minutes

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

14 vidéos Total 84 minutes

Module 11: Introduction to Week 11's Study 1 minute
Artificial Neural Network Introduction: Introduction 3 minutes
Artificial Neural Network Introduction: ANN Structure 5 minutes
Neural Network Training: Forward Propagation 6 minutes
Neural Network Training: Backward Propagation 13 minutes
Neural Network Training: More on Neural Network Training 8 minutes
Underfitting and Overfitting: Concepts 8 minutes
Underfitting and Overfitting: Methods to Improve Overfitting 9 minutes
ANN Implementation in MATLAB and Case Studies: Build an ANN Using MATLAB 6 minutes
ANN Implementation in MATLAB and Case Studies: A Case Study on Diabetes Diagnosis 1 minute
Tutorial: MATLAB Classification and Regression Learner Apps 7 minutes
Tutorial: Classification Learner 11 minutes
Tutorial: Export Model 3 minutes
Tutorial: Regression Learner 4 minutes

5 lectures Total 41 minutes

Materials on ANN fundamentals 10 minutes
Materials on Neural Network Training 10 minutes
Materials on Underfitting and Overfitting 10 minutes
Materials on ANN Implementation 10 minutes
MAT files for Module 11 Assignment 1 minute

1 devoir Total 30 minutes

Module 11 Quiz 30 minutes

1 élément d'application Total 30 minutes

Module 11 Assignment 30 minutes

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Instructeur

Bo Liu

University of Glasgow

3 Cours 3 363 apprenants

Offert par

University of Glasgow

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Révisé le 22 nov. 2025

The support from teaching staff is timely and helpful.

Révisé le 22 nov. 2025

Finished feeling confident to put “ML skills” on my CV.

Révisé le 21 nov. 2025

The pacing is perfect: conceptual overview first, then data prep, then deep dives—no cognitive overload at any point.

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