Support Vector Regression: Nu-SVR and RVR

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Related lectures (32)

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Support Vector Regression: Principles and Optimization

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Support Vector Regression: Kernel Tricks

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Models for Classification: Logistic Regression and SVM

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

Introduction to Machine Learning: Supervised Learning

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

Classification Algorithms: Generative and Discriminative Approaches

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

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.