Classification with GMM and kNN

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Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Explores the control of complexity in hypothesis spaces and the trade-off between approximation and estimation in risk decomposition.

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

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

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

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

Explores overparameterization, generalization, overfitting, underfitting, and implicit regularization in deep learning models.

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

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.