Lectures related to Decision boundary

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

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

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.

Introduction to Machine Learning

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

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 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.

Linear Models: Classification

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

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Statistical Inference and Machine Learning

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

Support Vector Machines: Basics and Applications Log in to Mediaspace to watch this video

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

Linear Models & k-NN Log in to Mediaspace to watch this video

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Support Vector Machines: Interactive Class Log in to Mediaspace to watch this video

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

SVM and Multiclass Classification Log in to Mediaspace to watch this video

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Support Vector Machines: Parameters, Solutions, and Boundaries Log in to Mediaspace to watch this video

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

Support Vector Machines: Theory and Applications Log in to Mediaspace to watch this video

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

Feature Expansion: Kernels and KNN Log in to Mediaspace to watch this video

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