Different Machine Learning Categories and Algorithms

1. Overview

At a high-level, machine learning is simply the study of teaching a computer program or algorithm how to progressively improve upon a set task that it is given. On the research side of things, machine learning can be viewed through the lens of theoretical and mathematical modeling of how this process works. However, more practically it is the study of how to build applications that exhibit this iterative improvement. There are many ways to frame this idea, but largely there are three major recognized categories: supervised learning, unsupervised learning, and reinforcement learning.

 

2. Description

2.1 Supervised Learning

2.1.1 Draft

• Predictive Model
• we have labeled data
• The main types of supervised learning problems include regression and classification problems

2.1.2 List of Common Algorithms

• Nearest Neighbor
• Naive Bayes
• Decision Trees
• Linear Regression
• Support Vector Machines (SVM)
• Neural Networks

2.1.3 Applications

• Advertisement Popularity
• Spam Classification
• Face Recognition

2.2 Unsupervised Learning

2.2.1 Draft

• Descriptive Model
• The main types of unsupervised learning algorithms include Clustering algorithms and Association rule learning algorithms.

2.2.2 List of Common Algorithms

• k-means clustering, Association Rules

2.2.3 Applications

• Recommender Systems

• Buying Habit

• Grouping User Logs

2.3 Semi-supervised Learning

2.4 Reinforcement Learning

2.4.1 List of Common Algorithms

• Q-Learning
• Temporal Difference (TD)
• Deep Adversarial Networks

2.4.2 Applications

• Video Games

• Industrial Simulation

• Resource Management

3. References

https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861

https://www.researchgate.net/figure/Different-Machine-Learning-Categories-and-Algorithms_fig2_326564924