Types of Machine Learning

Study Notes

Supervised Learning Overview

Supervised learning occurs when an algorithm learns from example data paired with corresponding target responses.
It is characterized by training models on labeled datasets, allowing the algorithm to make predictions based on prior learned patterns.

Goals of Supervised Learning

The primary goal is to accurately predict the output variable from input variables by generalizing from the training data.
It aims to minimize the error between predicted and actual target responses to improve model performance.

Comparison to Human Learning

Similar to human learning under the guidance of a teacher, supervised learning involves direct instruction through labeled datasets.
The algorithm is 'taught' using examples where the correct output is known, enhancing its understanding of input-output relationships.

Input and Output Variables

Input variables (features) are the data attributes used by the algorithm to make predictions.
The output variable (target) is the result the algorithm is trying to predict based on input variables.

Learning Mapping Functions

Supervised learning employs algorithms to learn the mapping function that connects input variables to output variables.
This enables the model to predict outcomes for new, unseen data, leveraging the learned associations from the training set.

Description

Test your knowledge of different types of machine learning in this quiz! Learn about supervised learning, unsupervised learning, reinforcement learning, and hybrid learning problems such as semi-supervised learning, self-supervised learning, and multi-instance learning.