Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
Explores non-linear SVM using kernels for data separation in higher-dimensional spaces, optimizing training with kernels to avoid explicit transformations.