Data Streams: Algorithms and Applications

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Related lectures (29)

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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.

Nearest Neighbor Classifier: Curse of Dimensionality

Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.

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.

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Clustering: Unsupervised Learning

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

Gaussian Naive Bayes & K-NN

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

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

K-Nearest Neighbors & Feature Expansion

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

K-Nearest Neighbors & Feature Expansion

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

Linear Models & k-NN

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