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Related lectures (32)

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Textual Data Analysis: Classification & Dimensionality Reduction

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

Document Classification: Features and Models

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

Document Classification

Explores document classification methods, including Naïve Bayes and word embeddings.

Information Retrieval Basics

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Document Classification: Transformers and MLPs

Explores transformers and MLPs for document classification, emphasizing their benefits over traditional methods.

Latent Semantic Indexing: Concepts and Applications

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Handling Text: Document Retrieval & Classification

Explores document retrieval, classification, sentiment analysis, and topic detection in text analysis using supervised learning and bag-of-words models.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Recommender Systems: Text Classification & Naïve Bayes

Explores text classification using Naïve Bayes in content-based recommenders.

Latent Semantic Indexing

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.