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Related lectures (32)

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Linear Systems: Modeling and Identification

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Linear Regression

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Dataset: Training, Validation and Test

Covers the basics of machine learning, including dataset division and model training.

Model Evaluation

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Linear Regression: Statistical Inference and Regularization

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

Generalized Linear Regression: Classification

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

Untitled

Regression: Interactive Lecture

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Machine Learning Fundamentals: Overfitting and Regularization

Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.

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