**Course Title:** MATLAB Programming: Applications in Engineering, Data Science, and Simulation **Section Title:** Machine Learning with MATLAB **Topic:** Supervised learning: Classification and regression. **Introduction** Supervised learning is a type of machine learning where the algorithm is trained on labeled data to make predictions on new, unseen data. In this topic, we will explore two fundamental supervised learning algorithms: classification and regression. We will discuss the concepts, techniques, and MATLAB implementations of these algorithms, along with practical examples and applications. **Classification** Classification is a supervised learning algorithm that predicts a categorical label or class for a given input. Common classification problems include: * Binary classification (e.g., spam vs. non-spam emails) * Multi-class classification (e.g., handwritten digit recognition) **Types of Classification Algorithms** 1. **Linear Classification**: Linear classification algorithms use a linear model to separate the classes. Some popular linear classification algorithms include: * Logistic Regression [1] * Linear Discriminant Analysis (LDA) * Support Vector Machines (SVMs) 2. **Non-Linear Classification**: Non-linear classification algorithms use a non-linear model to separate the classes. Some popular non-linear classification algorithms include: * Decision Trees * Random Forests * K-Nearest Neighbors (KNN) **MATLAB Implementation** MATLAB provides a range of tools and functions for classification, including: * `fitclinear`: Fits a linear classification model to the data * `fitcknn`: Fits a KNN classification model to the data * `fitctree`: Fits a decision tree classification model to the data * `fitcsvm`: Fits an SVM classification model to the data **Example: Classification using Logistic Regression** ```matlab % Load the dataset load fisheriris % Split the data into training and testing sets train_data = [inputs(1:80,:), outputs(1:80,:)]; test_data = [inputs(81:150,:), outputs(81:150,:)]; % Train a logistic regression model Mdl = fitclinear(train_data(:,1:4), train_data(:,5)); % Evaluate the model on the test data [label, scores] = predict(Mdl, test_data(:,1:4)); ``` **Regression** Regression is a supervised learning algorithm that predicts a continuous response variable for a given input. Common regression problems include: * Simple linear regression * Multiple linear regression * Polynomial regression **Types of Regression Algorithms** 1. **Linear Regression**: Linear regression algorithms use a linear model to predict the response variable. 2. **Non-Linear Regression**: Non-linear regression algorithms use a non-linear model to predict the response variable. **MATLAB Implementation** MATLAB provides a range of tools and functions for regression, including: * `fitlm`: Fits a linear regression model to the data * `fitnlm`: Fits a non-linear regression model to the data **Example: Regression using Linear Regression** ```matlab % Load the dataset load hald % Fit a linear regression model Mdl = fitlm(ingredients, heat); % Predict the response variable for new data new_ingredients = [1 1 1 1 1]; % Example new data new_heat = predict(Mdl, new_ingredients); ``` **Conclusion** In this topic, we have explored supervised learning algorithms for classification and regression. We have discussed the concepts, techniques, and MATLAB implementations of these algorithms, along with practical examples and applications. Through this topic, you should have gained a deep understanding of how to apply supervised learning algorithms to solve real-world problems. **External Resources** * [1] Logistic Regression in MATLAB: <https://www.mathworks.com/help/stats/fitclinear.html> * Machine Learning with MATLAB: <https://www.mathworks.com/products/machine-learning.html> **Practical Takeaways** * Classification algorithms can be used to predict categorical labels or classes for a given input. * Regression algorithms can be used to predict continuous response variables for a given input. * MATLAB provides a range of tools and functions for supervised learning, including `fitclinear`, `fitcknn`, `fitctree`, `fitcsvm`, `fitlm`, and `fitnlm`. **Next Topic: Unsupervised learning: Clustering and dimensionality reduction.** Leave a comment or ask for help if you have any questions about this topic.