Articles on: Basic concepts

1. Machine Learning Algorithms Basics

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. There are many different types of machine learning algorithms, but they can broadly be grouped into three categories: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning

Supervised learning is the most common type of machine learning and it involves training a model on a labeled dataset, where the desired output is already known. Common examples of supervised learning algorithms include linear regression, logistic regression, and decision trees. The basic idea behind supervised learning is to learn a mapping from input features to output labels, so that when new, unseen data is presented to the model, it can predict the correct output.

Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is not given labeled data, but instead must find patterns and relationships in the input data on its own. Common examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA), and autoencoders. The basic idea behind unsupervised learning is to discover hidden structure in the data, such as grouping similar examples together.

A Clustering Model from Richard Socher et al. (2013)

Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns by interacting with its environment and receiving feedback in the form of rewards or penalties. Common examples of reinforcement learning algorithms include Q-learning and SARSA. The basic idea behind reinforcement learning is to learn a strategy that maximizes the cumulative reward over time.

Reinforcement Learning

These are the basics of machine learning algorithm with a broad categorization, but there are many other sub categories and variations available depending on the problem you are trying to solve.

[1] Richard Socher et al., (2013), “Zero-Shot Learning Through Cross-Modal Transfer”, Proceedings of the 26th International Conference on Neural Information Processing Systems (1st Edition): 935-943.

Updated on: 30/01/2023

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