From average to online learning

Related lectures (31)

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Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Explains policy gradient for a single neuron, including writing policy, using the logistic function, and calculating the gradient.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.