Machine Learning and its Applications

Machine Learning and its Applications

本课程是 Applied AI for Engineers and Scientists: Foundations 专项课程的一部分

位教师：Bo Liu

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5个模块

深入了解一个主题并学习基础知识。

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2 周完成

在 10 小时一周

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5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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

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作业

5 项作业

授课语言：英语（English）

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本课程是 Applied AI for Engineers and Scientists: Foundations 专项课程专项课程的一部分

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该课程共有5个模块

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:

涵盖的内容

10个视频7篇阅读材料1个作业2个应用程序项目1个讨论话题

10个视频总计65分钟

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations6分钟
Module 7: Introduction to Week 7's Study1分钟
Machine Learning Fundamentals: What is Machine Learning6分钟
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (1)6分钟
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (2)8分钟
Mapping Inputs to Outputs: Data Representation10分钟
Mapping Inputs to Outputs: Parametric ML Model7分钟
Mapping Inputs to Outputs: Non-Parametric ML Model7分钟
Mapping Inputs to Outputs: Evaluate Output2分钟
MATLAB Implementation: Simple Linear Regression and KNN6分钟

7篇阅读材料总计56分钟

Specialization and Course Structure10分钟
Specialization Sample Certificate10分钟
How to Access MATLAB Online5分钟
Materials for Machine Learning Fundamentals10分钟
Materials for Mapping Inputs to Outputs10分钟
Materials about MATLAB Implementation10分钟
CSV File for Module 7 Assignment 1分钟

1个作业总计30分钟

Module 7 Quiz30分钟

2个应用程序项目总计60分钟

Module 7 Assignment 130分钟
Module 7 Assignment 230分钟

1个讨论话题总计10分钟

Module 7 Discussion on Machine Learning10分钟

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:

涵盖的内容

7个视频4篇阅读材料1个作业2个应用程序项目1个讨论话题

7个视频总计50分钟

Module 8: Introduction to Week 8's Study1分钟
ML Model Training Fundamentals: Parameters, Hyperparameters, and Loss Functions8分钟
ML Model Training Fundamentals: Loss Functions and Gradient Descent5分钟
ML Model Training Fundamentals: Gradient Descent10分钟
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation13分钟
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation5分钟
Summary of the ML Process5分钟

4篇阅读材料总计31分钟

Materials for ML model training10分钟
Materials on ML Model Evaluation Fundamentals10分钟
Materials for ML Process10分钟
CSV file for Module 8 Assignment1分钟

1个作业总计30分钟

Module 8 Quiz30分钟

2个应用程序项目总计60分钟

Module 8 Assignment 130分钟
Module 8 Assignment 230分钟

1个讨论话题总计10分钟

Module 8 Discussion on ML Model Quality10分钟

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:

涵盖的内容

8个视频6篇阅读材料1个作业3个应用程序项目1个讨论话题

8个视频总计38分钟

Module 9: Introduction to Week 9's Study2分钟
Basic Data Cleaning Review 0分钟
Distributions, Outliers and Their Removal: Gaussian Distribution, Skewness and Outliers6分钟
Distributions, Outliers and Their Removal: The Z-Score and IQR Method for Outliers Removal5分钟
Data Transform: Normalization, Standardization, Power Transform7分钟
Construct Training and Test Sets for Model Evaluation: Methods, Implementation, and Stratified Sampling5分钟
Construct Training and Test Sets for Model Evaluation: Cross-Validation6分钟
Data Preparation Overview3分钟

6篇阅读材料总计51分钟

Materials on Basic Data Cleaning 10分钟
Materials on Distributions, Outliers and Their Removal10分钟
Materials on Data Transform10分钟
Materials on Training/Test Sets Generation10分钟
Materials on Data Preparation10分钟
XLS file for Module 9 Assignment1分钟

1个作业总计30分钟

Module 9 Quiz30分钟

3个应用程序项目总计90分钟

Module 9 Assignment 130分钟
Module 9 Assignment 230分钟
Module 9 Assignment 330分钟

1个讨论话题总计10分钟

Module 9 Discussion on Data Preparation10分钟

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:

涵盖的内容

12个视频4篇阅读材料1个作业2个应用程序项目

12个视频总计79分钟

Module 10: Introduction to Week 10's Study1分钟
Support Vector Machine Fundamentals: Concepts11分钟
Support Vector Machine Fundamentals: Types of SVM4分钟
Support Vector Machines: Linear SVM of Hard Margin Classifier7分钟
Support Vector Machines: Linear SVM of Soft Margin Classifier11分钟
Support Vector Machines: Non-Linear SVM12分钟
Support Vector Machines: Multi-Class SVM8分钟
Support Vector Machine Implementation: MATLAB Implementation of SVM4分钟
Support Vector Machine Implementation: Iris Flower Example5分钟
Support Vector Machine Implementation: 2D Point Classification Example0分钟
Case Study 1: Banknote Classification (Linear SVM)4分钟
Case Study 2: Raisin Classification (Non-Linear SVM)7分钟

4篇阅读材料总计31分钟

Materials on Support Vector Machine Fundamentals10分钟
Materials on Support Vector Machines10分钟
Materials on Support Vector Machine Implementation and Case Studies10分钟
CSV file for Module 10 Assignment1分钟

1个作业总计30分钟

Module 10 Quiz30分钟

2个应用程序项目总计60分钟

Module 10 Assignment 130分钟
Module 10 Assignment 230分钟

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:

涵盖的内容

14个视频5篇阅读材料1个作业1个应用程序项目

14个视频总计84分钟

Module 11: Introduction to Week 11's Study1分钟
Artificial Neural Network Introduction: Introduction3分钟
Artificial Neural Network Introduction: ANN Structure4分钟
Neural Network Training: Forward Propagation5分钟
Neural Network Training: Backward Propagation13分钟
Neural Network Training: More on Neural Network Training8分钟
Underfitting and Overfitting: Concepts7分钟
Underfitting and Overfitting: Methods to Improve Overfitting8分钟
ANN Implementation in MATLAB and Case Studies: Build an ANN Using MATLAB6分钟
ANN Implementation in MATLAB and Case Studies: A Case Study on Diabetes Diagnosis1分钟
Tutorial: MATLAB Classification and Regression Learner Apps6分钟
Tutorial: Classification Learner10分钟
Tutorial: Export Model3分钟
Tutorial: Regression Learner3分钟

5篇阅读材料总计41分钟

Materials on ANN fundamentals10分钟
Materials on Neural Network Training10分钟
Materials on Underfitting and Overfitting10分钟
Materials on ANN Implementation10分钟
MAT files for Module 11 Assignment1分钟

1个作业总计30分钟

Module 11 Quiz30分钟

1个应用程序项目总计30分钟

Module 11 Assignment30分钟

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位教师

Bo Liu

University of Glasgow

3 门课程2,147 名学生

提供方

University of Glasgow

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