Concept

Decision boundary

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Linear Models for Classification

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

Linear Models: Classification Basics

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Linear Models for Classification: Part 3

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

Max-Margin Classifiers

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

Support Vector Machines: Interactive Class

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

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

SVM and Multiclass Classification

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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.

Support Vector Machines: Theory and Applications

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

Feature Expansion: Kernels and KNN

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

Introduction to Machine Learning: Supervised Learning

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

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.

Linear Models for Classification: Logistic Regression and SVM

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

Linear Classification Models: From Binary to Multiclass

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Supervised Learning: Classification and Regression

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

Feature Expansion and Kernels

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Linear Models: Classification

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