Document Retrieval and Classification

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Related lectures (48)

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Handling Text Data: Document Retrieval and Classification

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

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Vector Space Semantics (and Information Retrieval)

Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Nonlinear ML Algorithms

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Supervised Learning in Financial Econometrics Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Latent Semantic Indexing Log in to Mediaspace to watch this video

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

Regression: High Dimensions Log in to Mediaspace to watch this video

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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.

Linear Regression: Foundations and Applications Log in to Mediaspace to watch this video

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Cross-Validation: Techniques and Applications Log in to Mediaspace to watch this video

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Linear Regression: Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Back to Linear Regression Log in to Mediaspace to watch this video

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

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