Lectures related to Entity & Information Extraction

Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Entity & Information Extraction

Explores knowledge extraction from text, covering key concepts like keyphrase extraction and named entity recognition.

Information Extraction: Algorithms and Techniques

Explores algorithms and techniques for information extraction, including Viterbi algorithm, named entities recognition, and distant supervision.

Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Matrix Factorization: Information Extraction

Explores matrix factorization for information extraction, Bayesian ranking, and relation embeddings.

Information Extraction: Bootstrapping

Explores Information Extraction approaches like hand-written patterns and distant supervision, with examples of entity pairs matching patterns.

Information Extraction: Approaches and Techniques

Covers Information Extraction approaches, including hand-written patterns and supervised learning.

Supervised Learning in Asset Pricing

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Semantic Web & Information Extraction

Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.

Matrix Factorization: Linking Text to Knowledge Bases

Explores distant supervision for linking text to knowledge bases using entity extraction and classifiers.

Mathematics of Data: Models and Learning

Explores models, learning paradigms, and applications in Mathematics of Data.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Text Understanding

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.