Kernel Methods: Nonlinear Separation Surfaces

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Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Feature Expansion: Kernels and KNN

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

Introduction to Machine Learning: Linear Models

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

Support Vector Machines: SVM Basics

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Kernel Methods: Neural Networks

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

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Unsupervised Learning: Dimensionality Reduction

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

Kernel Regression: K-nearest Neighbors

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

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.