Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Cited by 0

Unsupervised Learning

Peter Dayan, Maneesh Sahani, Grégoire Deback · MPG.PuRe (Max Planck Society) · 1999

Adaptation is a ubiquitous neural and psychological phenomenon, with a wealth of instantiations and implications. Although a basic form of plasticity, it has, bar some notable exceptions, attracted computational theory of only one main variety. In this paper, we study adaptation from the perspective of factor analysis, a paradigmatic technique of unsupervised learning. We use factor analysis to re-interpret a standard view of adaptation, and apply our new model to some recent data on adaptation in the domain of face discrimination. 1

Cited by 33Open access

Unsupervised Learning

Phillip A. Laplante, Satish Srinivasan · 2023

Cited by 3

Unsupervised learning

Sadegh Raeisi, Sedighe Raeisi · 2023

Unsupervised techniques are another category of machine learning techniques. Unsupervised learning refers to wide categories of methods that are designed to work with and extract patterns from unlabeled datasets. This makes it one of the most important types of machine learning because most datasets are not labelled. Here we review some of the unsupervised techniques.

Cited by 0

Unsupervised Learning

Akshay Bhuvaneswari Ramakrishnan, S. Srijanani · Advances in computational intelligence and robotics book series · 2025

Unsupervised learning, an essential component of machine learning, has a substantial impact on the advancement and implementation of generative AI. Incorporating unsupervised learning into generative AI models has the potential to transform businesses by automating and improving creative processes. This chapter explores the fundamental principles, techniques, and progress in unsupervised learning. The authors delve into a range of methods and approaches, including clustering, dimensionality reduction, data mining, feature extraction, neural networks, and anomaly detection, emphasizing their use in generative models. This chapter provides a detailed explanation and use cases to demonstrate how unsupervised learning allows generative AI to produce new and high-quality outputs without the need for labeled data.

Cited by 4

Unsupervised Learning

2017

Unsupervised training methods can help to decide which pixels of the image belong to the background and which to the foreground. This chapter discusses two main characteristics that can be explored: the subspace structure of data and its clustering characteristics. The first tries to summarize the objects using a smaller number of features than the original number of measurements; the second tries to summarize the dataset using a smaller number of objects than the original number. The chapter also discusses feature reduction and clustering. The most popular unsupervised feature reduction method is principal component analysis (PCA). The chapter further discusses four methods for performing a clustering: hierarchical clustering, K-means clustering, mixtures of Gaussians and finally self-organizing maps. It concludes with a probabilistic version of the self-organizing map, the generative topographic mapping (GTM).

Cited by 3

Unsupervised Learning

Miroslav Kubat · 2021

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Unsupervised Learning

Miroslav Kubat · 2017

Cited by 6

Unsupervised Learning

Trevor Hastie, Robert Tibshirani, J. Friedman · Springer series in statistics · 2008

Cited by 443

Unsupervised Learning

Geoff Dougherty · 2012

Cited by 5

Unsupervised Learning

Marcus J. Neuer · 2024

Cited by 16

Unsupervised learning

Glenn Rowe · 1994

Abstract The neural networks we have considered so far have all required an external teacher to tell them the correct response for each input. The networks learn by adjusting their synaptic weights to make their outputs correspond more closely to that desired by the teacher.

Cited by 1

Unsupervised Learning

Nathalie Japkowicz, Zois Boukouvalas · Cambridge University Press eBooks · 2024

Cited by 0