Curse of dimensionality

Applied sciences
Information engineering
Data science
Dimensionality reduction

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Markov chains

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Classification with GMM and kNN

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

Gaussian Naive Bayes & K-NN

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

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Classification by Similarity

Delves into classification by similarity using prototypes to match noisy images.

Data Streams: Algorithms and Applications

Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.

Linear Models & k-NN

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

Johnson-Lindenstrauss Theorem

Covers the Johnson-Lindenstrauss Theorem, which embeds high-dimensional points into lower-dimensional space while preserving distances.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Page 2 of 2