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Concept

K-nearest neighbors algorithm

Related lectures (31)

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

K-Nearest Neighbors & Feature Expansion

Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.

Nearest Neighbor Search: Johnson-Lindenstrauss Lemma

Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.

KNN Classifier: Nearest Neighbor Approach

Explains the K-Nearest Neighbors classifier, assigning labels based on closest points and smoothing noise in labels.

K-Nearest Neighbors & Feature Expansion

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

KNN: Nearest Neighbor Classifier

Covers the k-Nearest-Neighbor classifier, hand-written digit recognition, multi-class k-NN, data reduction, applications, graph construction, limitations, and the curse of dimensionality.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

Locality Sensitive Hashing

Explores Locality Sensitive Hashing for nearest neighbor search and submodularity in hash functions.

Linear Models: Recap and Extensions

Covers linear models, multi-class classification, k-Nearest Neighbors, and feature expansion techniques.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Document Classification: Features and Models

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

Gaussian Naive Bayes & K-NN Log in to Mediaspace to watch this video

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Classification with GMM and kNN Log in to Mediaspace to watch this video

Covers classification using GMM and kNN, exploring boundaries, errors, and practical exercises.

Linear Models & k-NN Log in to Mediaspace to watch this video

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.