A Short Introduction to Machine Learning

A tutorial on machine learning for  interactive pedagogical systems

International Journal of Serious Games ◽

10.17083/ijsg.v5i3.256 ◽

2018 ◽

Vol 5 (3) ◽

pp. 79-112

Author(s):

Francisco S Melo ◽

Samuel Mascarenhas ◽

Ana Paiva

Keyword(s):

Machine Learning ◽

Intelligent Tutoring Systems ◽

Serious Games ◽

Intelligent Tutoring ◽

Short Introduction ◽

Tutoring Systems ◽

Key Concepts

This paper provides a short introduction to the field of machine learning for interactive pedagogical systems. Departing from different examples encountered in interactive pedagogical systems—such as intelligent tutoring systems or serious games—we go over several representative families of methods in machine learning, introducing key concepts in this field. We discuss common challenges in machine learning and how current methods address such challenges. Conversely, by anchoring our presentation on actual interactive pedagogical systems, highlight how machine learning can benefit the development of such systems.

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Reviews

The Leading Edge ◽

10.1190/tle39060438.1 ◽

2020 ◽

Vol 39 (6) ◽

pp. 438-439

Author(s):

Andreas Rüger ◽

John Brittan ◽

Robert Avakian

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Face Recognition ◽

Object Detection ◽

Image Classification ◽

Short Introduction ◽

Seismic Processing ◽

Oxford University ◽

Oxford University Press

Deep learning for computer vision: Image classification, object detection, and face recognition in Python, by Jason Brownlee, 2020, Machine Learning Mastery, 563 p., US$0 (eBook). Illustrated Seismic Processing: Volume 1: Imaging, by Stephen J. Hill and Andreas Rüger, ISBN 978-1-560-80361-4, 2019, Society of Exploration Geophysicists, 330 p., US$39 (members), US$72 (nonmembers). Geology: A Very Short Introduction, by Jan Zalasiewicz, ISBN 978-0-198-80445-1, 2018, Oxford University Press, 168 p., US$11.95 (print).

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A Short Introduction to Procedural Content Generation Algorithms for Videogames

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213019300011 ◽

2019 ◽

Vol 28 (02) ◽

pp. 1930001 ◽

Cited By ~ 1

Author(s):

Nicolas A. Barriga

Keyword(s):

Machine Learning ◽

Short Introduction ◽

Open Problems ◽

Content Creation ◽

Procedural Content Generation ◽

Different Types ◽

Videogame Industry ◽

Potential Impact ◽

Content Generation

One of the main costs of developing a videogame is content creation. Procedural Content Generation (PCG) can help alleviate that cost by algorithmically generating some of the content a human would normally produce. We first describe and classify the different types of content that can be automatically generated for a videogame. Then, we review the most prominent PCG algorithms, focusing on current research on search-based and machine learning based methods. Finally, we close with our take on the most important open problems and the potential impact solving them will have on the videogame industry.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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