Learning the Kernel: Convex Optimization

Related lectures (30)

Page 3 of 3

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

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

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

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

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

Covers linear classification using logistic regression, regularization, and multiclass classification.