Machine learning

Artificial Intelligence, Applied maths

Machine learning is about constructing algorithms that can approximate complex functions from observations of input/output pairs. Machine learning is related to Statistics since its goal is to make predictions based on data.


The goal is to approximate a target function \(f\) or signal \(S\). The output space is often continuous.


The goal is to approximate a target function that assigns label to input points.

Applications of machine learning

Here are a few applications of machine learning. There are many more:

Machine learning is very good at finding a function between two sets of data, no matter if such a function is grounded in reality or not. This is one of the many sources of algorithmic bias that can exist in ML models, which we need to understand and avoid when using such models in real-world settings.

Ethically debatable applications


  1. . . "Predicting Ethnicity with Data on Personal Names in Russia". SocArXiv. DOI.
  2. . . "Learning to Predict Gender from Iris Images". In 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 1–5. Crystal City, VA, USA: IEEE. DOI.
  3. . . "A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation from Facial Images". arXiv. DOI.
  4. . . "Accurate Machine Learning Prediction of Sexual Orientation Based on Brain Morphology and Intrinsic Functional Connectivity". Cerebral Cortex. DOI.
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