Lectures related to Linear Models & k-NN

Introduction to Machine Learning: Supervised Learning

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

Linear Models for Classification

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

Linear Models for Classification

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

Introduction to Machine Learning: Linear Models

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

Linear Classification Models: From Binary to Multiclass

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

Linear Models for Classification

Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.

Linear Models for Classification

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

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Support Vector Machine and Logistic Regression

Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

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: 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.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

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

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

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

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Machine Learning Review Log in to Mediaspace to watch this video

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Kernel Methods: SVM and Regression Log in to Mediaspace to watch this video

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

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