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Master advanced ML/DS statistics
·
Module 5
Linear Regression and Regularization
1
Understanding Linear Regression: Minimizing the Sum of Squared Errors
Formulate simple and multiple linear regression in terms of minimizing the sum of squared errors.
Interpreting Regression Model Outputs
Interpret the coefficients, intercept, and R-squared value of a fitted regression model.
3
Normal Equations: Closed-Form Least Squares
Explain the purpose of the normal equations as a closed-form solution for least squares.
4
Diagnosing Regression Assumptions with Residual Plots
Use residual plots to diagnose violations of regression assumptions.
5
Bias-Variance Tradeoff and Model Complexity
Explain the bias-variance tradeoff in the context of model complexity.
6
Regularization: Ridge and Lasso
Apply Ridge (L2) and Lasso (L1) regularization to mitigate overfitting and perform feature selection.
7
L1 vs. L2 Penalties: Coefficient Impact
Compare the effects of L1 and L2 penalties on model coefficients.
8
K-Fold Cross-Validation for Model Evaluation and Regularization Tuning
Use k-fold cross-validation to evaluate model performance and select regularization parameters.
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Optimization for Machine Learning
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Classification Models and Evaluation