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Related lectures (32)

Page 3 of 4

Handling Text Data: Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Vector Space Retrieval Exercise

Covers TF-IDF computation, document vectors, cosine similarity, and precision formulas.

Digital Urban History: Lausanne Time Machine

Delves into digitizing historical documents, standardizing document structure, and applying neural networks for text recognition and image segmentation.

Latent Dirichlet Allocation

Covers Latent Dirichlet Allocation, a state-of-the-art method for concept extraction using a probabilistic generative model.

Text-Based Information Retrieval

Covers the basic concepts of text-based information retrieval and how documents are indexed and retrieved based on user queries.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Gaussian Discriminant Rule: Classification & Boundaries

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Supervised Learning: k-NN and Decision Trees

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