Machine Learning and its Applications

Machine Learning and its Applications

This course is part of Applied AI for Engineers and Scientists: Foundations Specialization

Instructor: Bo Liu

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Assessments

5 assignments

Taught in English

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This course is part of the Applied AI for Engineers and Scientists: Foundations Specialization

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There are 5 modules in this course

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:

What's included

9 videos5 readings1 assignment2 app items1 discussion prompt

9 videosTotal 59 minutes

Module 7: Introduction to Week 7's Study1 minutePreview module
Machine Learning Fundamentals: What is Machine Learning6 minutes
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (1)6 minutes
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (2)8 minutes
Mapping Inputs to Outputs: Data Representation10 minutes
Mapping Inputs to Outputs: Parametric ML Model7 minutes
Mapping Inputs to Outputs: Non-Parametric ML Model7 minutes
Mapping Inputs to Outputs: Evaluate Output2 minutes
MATLAB Implementation: Simple Linear Regression and KNN6 minutes

5 readingsTotal 36 minutes

How to Access MATLAB Online5 minutes
Materials for Machine Learning Fundamentals10 minutes
Materials for Module 7-210 minutes
Materials about MATLAB Implementation10 minutes
CSV File for Module 7 Assignment 1 minute

1 assignmentTotal 30 minutes

Module 7 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 7 Assignment 130 minutes
Module 7 Assignment 230 minutes

1 discussion promptTotal 10 minutes

Module Discussion on Machine Learning10 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:

What's included

7 videos4 readings1 assignment2 app items1 discussion prompt

7 videosTotal 50 minutes

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

4 readingsTotal 31 minutes

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

1 assignmentTotal 30 minutes

Module 8 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 8 Assignment 130 minutes
Module 8 Assignment 230 minutes

1 discussion promptTotal 10 minutes

Module Discussion on ML Model Quality10 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:

What's included

8 videos6 readings1 assignment3 app items1 discussion prompt

8 videosTotal 38 minutes

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

6 readingsTotal 51 minutes

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

1 assignmentTotal 30 minutes

Module 9 Quiz30 minutes

3 app itemsTotal 90 minutes

Module 9 Assignment 130 minutes
Module 9 Assignment 230 minutes
Module 9 Assignment 330 minutes

1 discussion promptTotal 10 minutes

Module Discussion on Data Preparation10 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:

What's included

12 videos4 readings1 assignment2 app items1 discussion prompt

12 videosTotal 79 minutes

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

4 readingsTotal 31 minutes

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

1 assignmentTotal 30 minutes

Module 10 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 10 Assignment 130 minutes
Module 10 Assignment 230 minutes

1 discussion promptTotal 10 minutes

Module Discussion Applications of Support Vector Machines10 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:

What's included

14 videos5 readings1 assignment1 app item1 discussion prompt

14 videosTotal 84 minutes

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