Support Vector Machines: Theory and Applications

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Kernel Ridge Regression: Equivalence, Representer Theorem, and Kernel Trick

Explores Kernel Ridge Regression, the Representer Theorem, and the Kernel Trick in machine learning.

Unsupervised Learning: Dimensionality Reduction

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

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Kernels: Nonlinear Transformations

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Support Vector Machine: Model Storage, Memory Usage, and Energy Consumption

Explores SVM model storage, memory usage, training time complexity, and energy consumption estimation.

Linear Models for Classification

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Support Vector Machines: Basics and Applications

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Support Vector Regression: Principles and Optimization

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Linear Models for Classification

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