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Related lectures (30)

Page 3 of 3

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: Dual Formulation for Hard Margin

Explores the dual formulation of Support Vector Machines for hard margin classification.

Kernel Methods: SVM and Regression

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

Uniform convergence and No-Free-Lunch theorem

Explores uniform convergence, loss functions, and the No-Free-Lunch theorem in machine learning.

Perception: Image Classification Challenges

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

Support Vector Machines: Linear Separability

Explores linear separability in support vector machines, focusing on hyperplane separation and margin optimization.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Introduction to Supervised Learning and Decision Theory

Covers supervised learning, decision theory, risk minimization, and goal achievement.

Mathematics of Data: Overview and Examples

Covers empirical risk minimization, statistical learning, and examples of cancer prediction, house pricing, and image generation.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.