Reinforcement Learning for Data Science

Jonatan Barkan; Michal Moran; Goren Gordon

doi:10.1007/978-3-031-24628-9_24

Reinforcement Learning for Data Science

Jonatan Barkan, Michal Moran, Goren Gordon

Department of Industrial Engineering

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In the realm of data science, where big data abound, most machine learning methods fall into one of the two categories: supervised learning in which labeled data exist, i.e., for each input in the dataset there is a known output; unsupervised learning in which no label exists and patterns in the data are sought after.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Pages	537-557
Number of pages	21
ISBN (Electronic)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_24
State	Published - 1 Jan 2023

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

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10.1007/978-3-031-24628-9_24

Cite this

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Reinforcement Learning for Data Science

Abstract

All Science Journal Classification (ASJC) codes

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