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Related lectures (48)

Machine Learning Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Information Extraction: Algorithms and Techniques Log in to Mediaspace to watch this video

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

Information Extraction: Methods and Applications Log in to Mediaspace to watch this video

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

Neural Networks for NLP Log in to Mediaspace to watch this video

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

PCA: Key Concepts Log in to Mediaspace to watch this video

Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.

Decision Trees: Classification Log in to Mediaspace to watch this video

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

Recommender Systems: Text Classification & Naïve Bayes Log in to Mediaspace to watch this video

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

Word Embeddings: Modeling Word Context and Similarity Log in to Mediaspace to watch this video

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Matrix Factorization: Information Extraction Log in to Mediaspace to watch this video

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

Classification pipeline: building and evaluating Log in to Mediaspace to watch this video

Explains building and evaluating a classification pipeline using tweet data sets.

Machine Learning Applications: Regression and Classification Log in to Mediaspace to watch this video

Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.

Taxonomy Induction: Learning Hierarchical Structures Log in to Mediaspace to watch this video

Explores taxonomy induction, learning terms and relationships to construct hierarchical structures.

Information Extraction: Approaches and Techniques Log in to Mediaspace to watch this video

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

Taxonomy Induction: Relations Extraction and Graph Construction Log in to Mediaspace to watch this video

Covers relations extraction and graph construction in taxonomy induction, emphasizing noise reduction for accurate graphs.

Feature Expansion and Kernel Methods Log in to Mediaspace to watch this video

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Support Vector Machines: Basics and Applications Log in to Mediaspace to watch this video

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Feature Selection, Kernel Regression, Neural Networks Playground Log in to Mediaspace to watch this video

Covers feature selection, kernel regression, and neural networks through exercises.

Latent Semantic Indexing: Concepts and Applications Log in to Mediaspace to watch this video

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

Matrix Factorization: Optimization and Evaluation Log in to Mediaspace to watch this video

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

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