Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Pattern Recognition and Machine Learning
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2190 results in Pattern Recognition and Machine Learning

Page 1 of 110

Mathematical Methods in Data Science
  • Bridging Theory and Applications with Python
  • Sébastien Roch
  • Coming soon
  • Expected online publication date:
    September 2025
    Print publication:
    30 September 2025
  • Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.

Wireless Communications and Machine Learning

Introduction to Machine Learning
  • From Math to Code
  • Ruye Wang
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.

Scalable Monte Carlo for Bayesian Learning
  • Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.

Deep Learning Recommender Systems
  • Zhe Wang, Chao Pu, Felice Wang
  • Coming soon
  • Expected online publication date:
    May 2025
    Print publication:
    31 May 2025
  • Recommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

Machine Learning in Quantum Sciences

Machine Learning with Python
  • Principles and Practical Techniques
  • Parteek Bhatia
  • Coming soon
  • Expected online publication date:
    February 2025
    Print publication:
    28 February 2025
  • Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner.

  • 12 - Conclusion

  • from Part IV - Evaluation from a Practical Perspective
  • Nathalie Japkowicz, American University, Washington DC, Zois Boukouvalas, American University, Washington DC
  • Book:
    Machine Learning Evaluation
    Published online:
    07 November 2024
    Print publication:
    21 November 2024, pp 342-358

  • Summary

    Chapter 12 is the conclusion. It presents a discussion of how the components of performance evaluation for learning algorithms discussed throughout the book unify into an overall framework for in-laboratory evaluation. This is followed by a discussion of how to move from a laboratory setting to a deployment setting based on the material covered in the last part of the book. We then discuss the potential social consequences of machine learning technology deployment together with their causes, and advocate for the consideration of these consequences as part of the evaluation framework. We follow this discussion with a few concluding remarks.

  • 4 - Traditional Machine Learning Evaluation

  • from Part I - Preliminary Considerations
  • Nathalie Japkowicz, American University, Washington DC, Zois Boukouvalas, American University, Washington DC
  • Book:
    Machine Learning Evaluation
    Published online:
    07 November 2024
    Print publication:
    21 November 2024, pp 51-80

  • Summary

    Chapter 4 reviews frequently used machine learning evaluation procedures. In particular, it presents popular evaluation metrics for binary and multi-class classification (e.g., accuracy, precision/recall, ROC analysis), regression analysis (e.g., mean squared error, root mean squared error, R-squared error), clustering (e.g., Davies–Bouldin Index). It then reviews popular resampling approaches (e.g.,holdout, cross-validation) and statistical tests (e.g., the t-test and the sign test). It concludes with an explanation of why it is important to go beyond these well-known methods in order to achieve reliable evaluation results in all cases.

  • 6 - Resampling

  • from Part II - Evaluation for Classification
  • Nathalie Japkowicz, American University, Washington DC, Zois Boukouvalas, American University, Washington DC
  • Book:
    Machine Learning Evaluation
    Published online:
    07 November 2024
    Print publication:
    21 November 2024, pp 128-153

  • Summary

    Chapter 6 addresses the problem of error estimation and resampling in both a theoretical and practical manner. The holdout method is reviewed and cast into the bias/variance framework. Simple resampling approaches such as cross-validation are also reviewed and important variations such as stratified cross-validation and leave-one-out are introduced. Multiple resampling approaches such as bootstrapping, randomization, and multiple trials of simple resampling approaches are then introduced and discussed.

  • 2 - Statistics Overview

  • from Part I - Preliminary Considerations
  • Nathalie Japkowicz, American University, Washington DC, Zois Boukouvalas, American University, Washington DC
  • Book:
    Machine Learning Evaluation
    Published online:
    07 November 2024
    Print publication:
    21 November 2024, pp 8-32

  • Summary

    Chapter 2 reviews the principles of statistics that are necessary for the discussion of machine learning evaluation methods, especially the statical analysis discussion of Chapter 7. In particular, it reviews the notions of random variables, distributions, confidence intervals, and hypothesis testing.

Page 1 of 110