Inference for Max-Stable Processes

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Statistical Theory: Inference and Sufficiency

Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.

Data Representation: BoW and Imbalanced Data

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Linear Regression: Statistical Inference and Regularization

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

L-Moment Estimation: Probability-Weighted Moments

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Data Representations & Processing

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Probability and Statistics: Basics and Applications

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Data Representations and Processing

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Model Assessment and Hyperparameter Tuning

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

Mathematics of Data: Models and Estimators

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.