An overview of AI and Machine Learning methods: motivations, concepts, and examples

Abstract

What is artificial intelligence? Big Data? Machine learning? It may seem very difficult to define these terms and identify their use in real life, aside from the hype surrounding them over the past ten years. All of these terms are interconnected - sometimes they intersect, often they are included in each other. The main focus of this presentation is to present ‘Machine learning' methods, which can be defined as algorithms that learn patterns (a function) about a particular phenomenon (an output variable), based on examples (training data), often without any a priori on the shape of these patterns. Therefore, they are a broad family of methods that include traditional statistical methods such as linear regression models, but which often offer more flexibility in terms of hypothesis and complex data settings. Multiple frameworks exist, based on how many examples are available (size of the training data) and the kind of modeling application one wants to perform (e.g., classification, regression, clustering). Many different families of methods exist for each modeling application, which offers a tremendous amount of different algorithms, each having advantages and drawbacks. In practice, easy-to-use libraries were developed in popular programming languages such as Python and R, offering implementations of the majority of essential algorithms.