Akaike information criterion

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Related lectures (30)

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Time Series Analysis: ARMA Models

Explores ARMA models in time series analysis, covering model selection, forecasting, and precision assessment.

Equivalence Criterion: Proof and Examples

Covers the equivalence criterion for series convergence and provides illustrative examples.

Classification with GMM and kNN

Explores Mixture of Gaussians, Bayes' classification, kNN, overfitting, and computational costs.

Bias-Variance Trade-off

Explores the impact of model complexity on prediction quality through the bias-variance trade-off, emphasizing the need to balance bias and variance for optimal performance.

Overfitting vs Underfitting

Explores the impact of model complexity on error rates in machine learning.

Generalization and Overfitting

Covers generalization, overfitting, and model complexity in machine learning.

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Generalization in Learning with Random Features

Explores generalization in machine learning, focusing on underfitting and overfitting trade-offs, teacher-student frameworks, and the impact of random features on model performance.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.