Kernel Methods: Recap and Applications

Related lectures (32)

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

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