Machine Learning in Credit Risk Modeling

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Related lectures (31)

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Linear Regression: Statistical Inference and Regularization

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

Machine Learning in Credit Risk Modeling

Compares machine learning with traditional models in credit risk modeling, emphasizing non-linear relationships and predictive enhancements.

Risk and Return Measures

Covers risk and return measures, unbiasedness, and consistency of estimators.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Estimation and Method of Moments

Covers the definition of statistics and estimators, examples of estimators, and the method of moments.

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.

Eliminating Nuisance Parameters: Lemmas in Statistical Inference

Explores the elimination of nuisance parameters in statistical models using Lemmas 14 and 15.

Probability and Statistics: Basics and Applications

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

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Statistical Analysis: Data Exploration and Inference

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.