Lectures related to Support Vector Machines: Linear Separability

Support Vector Machines: Maximizing Margin

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Support Vector Machines: Theory and Applications

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Support Vector Machines

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Perceptron: Part 2

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Support Vector Machines: SVM Basics

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

Support Vector Machines: Basics and Applications

Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.

Support Vector Machines: Parameters, Solutions, and Boundaries

Explores SVM parameters, solutions, and decision boundaries, including the uniqueness of solutions and the impact of kernel width.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Support Vector Machine: Primal Formulation with Hard Margin

Covers the Support Vector Machine with a hard margin formulation and the importance of maximizing the margin between classes.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Support Vector Machines: Definition and Separation Hyperplane

Covers the history, linear separability, hyperplanes, and support vectors in Support Vector Machines.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.