Lecture on Unsupervised Learning

These flashcards cover key vocabulary terms and concepts related to unsupervised learning as discussed in the lecture.

Unsupervised Learning

A type of machine learning where the model is trained on data without labeled responses.

Filtering

A technique in unsupervised learning aimed at cleaning up input patterns, allowing models to recognize objects under varying conditions.

Reconstruction

A method used to fill in missing information in incomplete data, such as predicting obscured features in images.

Information Compression

The process of reducing the size of data while maintaining its integrity, often used in file formats to save storage.

Clustering

An unsupervised learning technique that groups similar data points together based on certain features or distance metrics.

Anomaly Detection

The identification of rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

Autoencoder

A type of neural network used to learn efficient representations of data, typically for dimensionality reduction or feature learning.

Denoising Autoencoder

An autoencoder designed to remove noise from input data, learning to reconstruct clean inputs from corrupted versions.

Latent Semantic Indexing (LSI)

A technique in natural language processing that uncovers latent relationships between documents and terms using singular value decomposition.

TF-IDF (Term Frequency-Inverse Document Frequency)

A statistical measure used to evaluate how important a word is to a document in a collection or corpus.

Singular Value Decomposition (SVD)

A mathematical technique used to factorize a matrix into three other matrices, widely used for compression and noise reduction.

Euclidean Distance

A common distance metric used to measure the straight-line distance between two points in Euclidean space.

Regularization

A technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.