Akaike information criterion

Formal sciences
Statistics
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Cross-validation

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Flexibility of Models & Bias-Variance Trade-Off

Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.

Generalization Theory

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Polynomial Regression: Overview

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Bias-Variance Tradeoff in Machine Learning

Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Deep Learning: Designing Neural Network Models

Covers the design and optimization of neural network models in deep learning.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.