Supervised learning and unsupervised learning new 2024.pptx

Supervised learning and unsupervised learning

1. Supervised learning and unsupervised learning Supervised learning and unsupervised learning are two main types of machine learning, each with distinct characteristics and use cases. Here's a breakdown of their differences:

2. Data Labeling Supervised Learning : Uses labeled data, meaning each training example is paired with an output label. For example, in image classification, each image (input) has a corresponding category (output label). Unsupervised Learning : Uses unlabeled data, meaning the algorithm must find patterns and relationships on its own, without any specific guidance on what to look for.

3. Goal Supervised Learning : The goal is to learn a mapping function from input variables to output variables, enabling the model to predict outputs for new inputs. It's like learning with a teacher. Unsupervised Learning : The goal is to discover hidden patterns or intrinsic structures in the data. It's like learning without a teacher.

4. Types of Problems Supervised Learning : Typical tasks include classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised Learning : Typical tasks include clustering (e.g., customer segmentation), dimensionality reduction (e.g., Principal Component Analysis), and association (e.g., market basket analysis).

5. Evaluation Supervised Learning : Model performance can be easily evaluated using the labeled data (e.g., accuracy, precision, recall, F1 score). Unsupervised Learning : Evaluation is more challenging due to the absence of ground truth labels. Often, domain experts must manually evaluate the results.

6. Examples of Algorithms Supervised Learning : Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Naive Bayes. Unsupervised Learning : K-Means Clustering, Hierarchical Clustering, DBSCAN, Principal Component Analysis (PCA), and Autoencoders.