Optimization in Machine Learning

Related lectures (31)

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

Covers linear models for classification, focusing on logistic regression and support vector machines.

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

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

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

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

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

Covers the probabilistic model for linear regression and the importance of regularization techniques.