May 1, 2024
Updated May 11, 2025
41 minute read
K-Nearest Neighbors, often abbreviated as KNN, is a foundational algorithm in the world of machine learning. At its core, KNN operates on the simple idea that similar things exist in close proximity. In other words, data points that are alike will tend to be near each other in a given space. This algorithm can be used to classify new, unseen data points based on the "company they keep" or to predict a value based on the values of their closest neighbors. It's a type of "lazy learning" because it doesn't build a model beforehand; instead, it stores the entire training dataset and performs calculations only when a prediction is needed.
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Reading list
We've selected 43 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
K-Nearest Neighbors.
This foundational text in the field of statistical learning, covering a wide range of topics including KNN. It provides a rigorous treatment of the theoretical underpinnings of machine learning algorithms. While mathematically demanding, it is an invaluable reference for those seeking a deep understanding.
A comprehensive introduction to pattern recognition and machine learning, with a strong emphasis on a probabilistic perspective. KNN is discussed within this framework. widely-used textbook for advanced undergraduates and graduate students and serves as an excellent reference.
The third edition of this popular book, updated to include TensorFlow 2, continues to be a strong resource for learning machine learning with Python. It covers K-Nearest Neighbors and its implementation in scikit-learn, making it highly relevant for current practice and deepening understanding through code.
A practical guide to implementing machine learning algorithms, including KNN, using popular Python libraries. is excellent for those who want to gain hands-on experience and see how KNN is applied in practice. It's well-suited for undergraduates and professionals.
A comprehensive and advanced text that takes a probabilistic approach to machine learning. KNN is discussed within this broader framework. is suitable for graduate students and researchers due to its depth and mathematical rigor.
This is the Python version of the popular 'An Introduction to Statistical Learning'. It covers the same fundamental concepts, including KNN, but with practical examples and code in Python, making it highly relevant for those using Python for machine learning.
Provides a broad introduction to the field of machine learning, covering various algorithms including KNN. It aims to make the fundamentals accessible to students with a background in computer programming, probability, calculus, and linear algebra. It is often used as a textbook.
Is specifically focused on the K-Nearest Neighbor algorithm, covering its fundamentals and applications. It provides a dedicated resource for those who want to delve deeply into this particular algorithm. It covers both theoretical aspects and real-world examples.
A less mathematically intensive introduction to statistical learning compared to 'The Elements of Statistical Learning', making it more accessible to a wider audience. It covers KNN and provides practical examples using the R programming language. Suitable for advanced undergraduates and those new to the field.
Provides a theoretical foundation for machine learning algorithms, including KNN. It delves into the principles and mathematical derivations. It is geared towards advanced undergraduates and graduate students seeking a deeper theoretical understanding.
Offers a broad overview of data mining concepts and techniques, including classification methods like KNN. It useful reference for understanding the practical applications of KNN in data analysis and is often used in data mining courses.
Covers a wide range of machine learning topics and algorithms, with implementations in Python using libraries like scikit-learn. It includes a discussion of KNN and provides practical examples for applying it to real-world problems.
Provides a comprehensive overview of machine learning, including a chapter on k-Nearest Neighbors that covers the basics of the algorithm, as well as more advanced topics such as hyperparameter tuning and ensemble methods.
Provides a strong foundation in the fundamental concepts and techniques of machine learning for predictive analytics. It covers various algorithms, including likely discussions on instance-based methods like KNN, with worked examples and case studies to illustrate their application. It's good for gaining a broad understanding with practical context.
Focuses on the process of building predictive models, including the use of KNN. It provides practical guidance on data preprocessing, model building, and evaluation. It's a valuable resource for practitioners and those interested in applied machine learning.
Introduces machine learning algorithms with a focus on applications in information security. It explicitly mentions covering K-Nearest Neighbor (k-NN) and provides realistic examples, making it valuable for understanding the practical use of KNN in a specific domain.
Covers data mining techniques with a focus on practical tools and methods, including KNN. It provides a good balance of concepts and practical implementation using the Weka software. Useful for understanding the application of KNN in data mining tasks.
Provides a comprehensive overview of machine learning, with a focus on using Python. It includes a chapter on k-Nearest Neighbors that covers the basics of the algorithm, as well as more advanced topics such as nearest neighbor search and metric learning.
This is the introductory volume to Kevin Murphy's comprehensive work on probabilistic machine learning. It provides a solid foundation in the probabilistic concepts and models that underpin many machine learning techniques, offering a different perspective that can deepen the understanding of algorithms like KNN.
Provides a practical introduction to machine learning, with a focus on using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It includes a chapter on k-Nearest Neighbors that covers the basics of the algorithm, as well as more advanced topics such as nearest neighbor graphs and manifold learning.
Provides a comprehensive overview of machine learning, including a chapter on k-Nearest Neighbors that covers the basics of the algorithm, as well as more advanced topics such as nearest neighbor search and metric learning.
Provides a rigorous and in-depth theoretical treatment of nearest neighbor methods, including KNN. It covers statistical, probabilistic, combinatorial, and geometric aspects. It specialized text suitable for researchers and those focusing specifically on the theoretical underpinnings of KNN.
This textbook is designed for engineering students and provides an introduction to machine learning concepts, including a section on KNN. It aims to make the material accessible and relevant for an engineering audience.
Delves into the details of various machine learning algorithms, likely including a dedicated section on K-Nearest Neighbors. It would be useful for deepening the understanding of the algorithm's mechanics and variations.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/2rofto/k
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