Module 1: Exploratory Analysis

• Exercise 1: EDA Exploratory Analysis

Module 2: Feature engineering

• Exercise 2: Feature Engineering

• Exercise 3: Detection and treatment of Advanced Outliers

• Exercise 4: Multivariate model of imputation of missing values

• Exercise 5: Univariate analysis in percentiles in R

• Exercise 6: Continuous variable optimal univariate analysis in Excel

Module 3: Machine Learning

• Exercise 7: Segmentation data using K-Means

• Exercise 8: Credit Scoring Support Vector Machine

• Exercise 9: Credit Scoring Boosting

• Exercise 10: Credit Scoring Bagging

• Exercise 11: Credit Scoring Random Forest, R and Python

• Exercise 12: Credit Scoring Gradient Boosting Trees

Module 4: Deep Learning

• Exercise 14: Credit Scoring using Deep Learning Feed Forward

• Exercise 15: Credit Scoring using Deep Learning CNN

• Exercise 16: Credit Scoring using Deep Learning LSTM

• Exercise 17: Credit Scoring using GANs

Module 5: Tuning Hyperparameters in Deep Learning

• Exercise 18: Tuning hyperparameters in Xboosting, Random Forest and SVM models for credit scoring

• Exercise 19: Tuning hyperparameters in Deep Learning model for credit scoring

Module 6: The development process of the scorecard

• Exercise 20: Creating a scorecard using Excel, R, and Python

Module 7: Quantum Computing and Algorithms

• Exercise 21: Quantum operations

Module 8: Development of Credit Scoring using Quantum Machine Learning

• Exercise 22: Quantum Support Vector Machine to develop credit scoring model

• Exercise 23: Quantum Feed Forward Neural Networks to develop a credit scoring model and PD estimation

• Exercise 24: Quantum Convoluted Neural Networks to develop a credit scoring model and PD estimation

Module 9: Tensor Networks for Machine Learning

• Exercise 25: Construction of credit scoring and PD using tensor networks

Module 10: Probabilistic Machine Learning

• Exercise 26: Gaussian process for regression

• Exercise 27: Bayesian neural networks

Module 11: Advanced Validation of AI Models

• Exercise 28: Validation and diagnosis of advanced credit scoring models

Module 12: Probability of Default PD

• Exercise 29: Modeling the Margin of Caution PD

Module 14: Econometric and AI Models of PD

• Exercise 30: Using COX Regression to estimate the PD

• Exercise 31: Using logistic regression with panel data to estimate PD

• Exercise 33: Using Bayesian Logistic Regression to estimate PD

• Exercise 34: Using PD LASSO regression to estimate PD

• Exercise 35: Using Random Forest Survival to estimate PD

• Exercise 36: Using Cox Xboost to estimate PD

• Exercise 37: Using Deep Learning survival to estimate PD

• Exercise 38: Using Feed Forward NN to estimate PD

• Exercise 39: Using Quantum Neural Networks to estimate PD

Module 15: PD Calibration

• Exercise 40: PD calibration using Platt scaling and isotonic regression

• Exercise 41: PD calibration using Gaussian Process Regression

• Exercise 42: Calibration of the PD asymmetric Laplace distribution

Module 16: Bayesian PD and Gaussian Process

• Exercise 43: Logistic Model Bayesian PD in Python

• Exercise 44: PD using MCMC in R

• Exercise 45: PD using Process Gaussian Regression

Module 17: Low Default Portfolio PD (PD LDP)

• Exercise 46: PD LDP confidence interval approach in R

• Exercise 47: Multiperiod confidence interval approach PD LDP

• Exercise 48: Neutral Bayesian PD in R

• Exercise 49: Conservative Bayesian PD in R

• Exercise 50: Generating synthetic data with GAN for estimating PD

Module 18: Transition Matrices and Temporary Structure of PD

• Exercise 51: Transition Matrix analysis (cohort and duration) in Python

• Exercise 52: Calibration of the temporal structure of the PD

Module 19: Lifetime PD Models

• Exercise 53: Vintage EMV Decomposition model for PD Lifetime

• Exercise 54: Multinomial regression for estimating PD Lifetime

• Exercise 55: Multi-State Markov Model for estimating PD Lifetime

• Exercise 56: Matrix ASRF model for estimating PD Lifetime

• Exercise 57: SVM in Python for estimating PD Lifetime

• Exercise 58: Neural Network in Python for estimating PD Lifetime

• Exercise 59: Quantum Neural Network in Python for estimating PD Lifetime

• Exercise 60: Quantum SVM in Python for estimating PD Lifetime

Module 20: Advanced Calibration Lifetime PD

• Exercise 61: Nelson–Siegel calibration for Lifetime PD

• Exercise 62: Gamma Adjustment calibration

• Exercise 63: Factor Fit calibration

• Exercise 64: Vasicek model calibration

Module 21: Estimating Lifetime PD using Quantum Computing

• Exercise 65: Lifetime PD estimation using quantum enhancements

Module 22: LGD IRB in Retail Portfolios

(No exercises listed)

Module 23: Econometric and Machine Learning LGD Models

• Exercise 66: Logistic, Linear and Beta regression for LGD

• Exercise 67: Random Forest, XGBoost and Deep Learning for LGD

• Exercise 68: Comparison of LGD models via calibration and precision tests

Module 24: LGD models in IFRS 9

• Exercise 69: Tobit regression in R for LGD IFRS 9

• Exercise 70: Neural Networks for LGD IFRS 9

• Exercise 71: Support Vector Machine for LGD IFRS 9

Module 25: Advanced EAD and CCF modeling in IFRS 9

• Exercise 72: OLS Regression Model for CCF

• Exercise 73: CCF Logistic Regression Model

• Exercise 74: Neural Networks and SVM for CCF

Module 26: Prepayment Rate Modeling

• Exercise 75: IFRS 9 prepayment model for mortgages (R and Excel)

Module 27: Lifetime EAD for credit lines

• Exercise 76: Lifetime EAD model for individual lines of credit

• Exercise 77: Vintage Lifetime EAD/ECL model for credit line pools

Module 28: PD Backtesting

• Exercise 78: PD backtesting in Excel

Module 29: LGD Backtesting

• Exercise 79: Comparison of LGD model performance using calibration and precision tests

Module 30: Deep Learning for Macro-Economic Dynamics Modeling

• Exercise 80: Neural networks for Bellman equation macroeconomic model

Module 31: AI for Forecasting & Stress Testing PD and LGD

• Exercise 81: Forecasting PD using GAN Neural Networks

• Exercise 83: Forecasting PD using LSTM

• Exercise 84: Forecasting PD using Quantum LSTM

• Exercise 85: Stress testing Charge-off model using RNN LSTM

• Exercise 86: Forecasting PD with Pandemic & Climate variables (RNN LSTM)

• Exercise 87: Forecasting PD using Quantum GAN

• Exercise 88: Forecasting PD using Transformers

• Exercise 89: Bayesian LSTM for Stress Testing PD

Module 32: Classical Stress Testing of PD & LGD

• Exercise 90: Multiyear stress test

• Exercise 91: Stress Testing PD (Excel & SAS – CPV model)

• Exercise 92: Stress Testing ECL using matrices & time series

• Exercise 93: Stress Testing using linear regressions

• Exercise 94: Stress Test LGD

• Exercise 95: Joint Stress Test of PD & LGD

• Exercise 96: Stress Testing Uncertainty in R

Module 33: Stress Testing in Corporate Portfolios

• Exercise 97: Stress Testing PD & transition matrices using ASRF (SAS, R, Excel)

Module 34: Stress Testing under ECL IFRS 9

• Exercise 98: Global ECL Stress Testing (R & Excel)

Module 35: Quantum Computing for Stress Testing

• Exercise 99: DGSE model using deep learning

• Exercise 100: Quantum Monte Carlo Simulation vs Classical Monte Carlo

Module 36: Significant Increase in Credit Risk SICR

• Exercise 101: SICR estimation using ROC discriminant power test

Module 37: Models for Lifetime ECL

• Exercise 102: Lifetime ECL estimation for a consumer credit portfolio (R, Excel, VBA)

Module 38: Validation of ECL

(No exercises listed)

Module 39: Generative AI & Generative AI in IFRS 9

• Exercise 103: Embeddings for Words, sentences, question answering

• Exercise 104: Embedding Visualization

• Exercise 105: Embeddings on Large Dataset

• Exercise 106: Prompt engineering

• Exercise 107: Advanced Prompting Techniques

• Exercise 108: LLMs in Credit Rating

• Exercise 109: Modeling Lifetime PD using generative AI

• Exercise 110: Transformers for forecasting Lifetime PD

• Exercise 111: Analysis of ECL results using generative AI

• Exercise 112: Diffusion LLMs applied to ECL IFRS 9

Module 1: Directives on the Accounting of Expected Credit Losses of Basel III and IFRS 9

(No specific exercises listed.)

Module 2: Phases of IFRS 9

(No specific exercises listed.)

Module 3: Impact of COVID-19 on Credit Risk

• Exercise 1: Modeling and projection of contagion curves in Python

Module 4: Impact of Covid-19 on IFRS 9 provisions

(No specific exercises listed.)

Module 5: Preparation of Econometric Models

• Exercise 2: Detection of non-stationary series and cointegration

• Exercise 3: Measurement of multivariate colinearity of logistic and linear regression model

Module 6: Advanced Projection Models

• Exercise 4: GARCH volatility forecasting in Python

• Exercise 5: Forecasting multivariate volatility GARCH in R

• Exercise 6: Forecasting financial series with Machine Learning using Python

• Exercise 7: Forecasting financial series and indices using Recurrent Neural Networks in Python

• Exercise 8: Pandemic Forecasting using RNN LSTM in Python

• Exercise 9: Charge-off model with VAR and VEC

• Exercise 10: Charge-off model with RNN LSTM

Module 7: Impact of COVID-19 on ECL models

• Exercise 11: Roll rates model and post Covid-19 migration matrices

Module 8: Analysis of Scenarios under COVID-19

• Exercise 12: Macroeconomic scenarios of associated macroeconomic variables using DSGE

Module 9: Loss Forecasting Models

• Exercise 14: Roll Rates in Excel

• Exercise 15: Multivariate Time Series of Roll Rates in R

• Exercise 16: Markov processes in SAS and Excel

• Exercise 17: Charge-off Deep Learning Model

• Exercise 18: Charge-off Model with macroeconomic variables linked to COVID-19

Module 10: Default IRB Probability

(No specific exercises listed.)

Module 11: PD Estimation using Credit Scoring

• Exercise 19: Advanced Credit Scoring Construction in Python and R

• Exercise 20: PD calibration with COX regression in R

• Exercise 21: PD Calibration with Panel Data Regression in R

• Exercise 22: Calibration of PD with LASSO logistic model in R

• Exercise 23: Calibration of PD with Bayesian probit regression in R

• Exercise 24: PD calibration with Bayesian COX regression in R

Module 12: PD Bayesiana

• Exercise 25: Bayesian PD of logistic model in Python

• Exercise 26: Bayesian PD of probit model in R

Module 14: Structural Models EDF and models of Reduced form

• Exercise 27: Estimate of EDF and Distance to Default (DD) of companies and sovereigns

• Exercise 28: Impact on PD curves due to the pandemic

Module 15: Low Default Portfolio PD (PD LDP)

• Exercise 29: Confidence interval approach PD LDP in R

• Exercise 30: Multiperiod confidence interval approach PD LDP

• Exercise 31: Neutral Bayesian PD in R

• Exercise 32: Conservative Bayesian PD in R

• Exercise 33: Bayesian PD with Expert criterion in R

Module 16: Transition Matrices and PD Temporal Structure

• Exercise 34: Transition Matrix analysis and error exercise using cohort and duration approach in Python

• Exercise 35: Calibration of the PD temporal structure

Module 17: Forecasting of PD IFRS 9

• Exercise 36: Forecasting the PD using VARMAX in R

• Exercise 37: PD forecasting using ASRF in R and Excel

Module 18: Lifetime PD Models

• Exercise 38: Lifetime PD using vintage EMV Decomposition with pandemic data

• Exercise 39: Lifetime PD using multinomial regression in R

• Exercise 40: Lifetime PD using Markov model

• Exercise 41: Lifetime PD using matrix ASRF model

• Exercise 42: Lifetime PD using extrapolation approach

• Exercise 43: Lifetime PD using SVM in Python

• Exercise 44: Lifetime PD using Deep Learning in Python and PD calibration

Module 19: PD IFRS 9 for LDP portfolios

• Exercise 45: Lifetime PD model in LDP portfolio

Module 20: LGD in Retail portfolios and IRB companies

• Exercise 46: Estimation and analysis of LGD and Exposure-Weighted Average LGD

Module 21: Econometric Models for LGD

• Exercise 47: Logistic and linear regression LGD in SAS

• Exercise 48: LGD Neural Networks

• Exercise 49: Beta Regression Model LGD in R and SAS

• Exercise 50: Comparison of the performance of the models using calibration and precision tests

Module 22: LGD for IFRS 9

• Exercise 51: Estimation and adjustments for LGD IFRS 9 using Tobit regression in R

• Exercise 52: Censored Regression Model LGD in R

• Exercise 53: LGD IFRS 9 estimation using LASSO regression in Python

• Exercise 54: Estimate LGD IFRS 9 using SVM

• Exercise 55: Estimate LGD IFRS 9 using Neural Networks

Module 23: Advanced EAD and CCF modeling for IRB

• Exercise 56: OLS regression model for CCF in Excel

• Exercise 57: CCF logistic regression model in Python

• Exercise 58: Neural Networks and SVM CCF in R

• Exercise 59: Beta Regression Model CCF in R

• Exercise 60: Comparison of the performance of EAD models

Module 24: Contractual Options

• Exercise 61: IFRS 9 prepaid model for mortgages in R and Excel

Module 25: EAD Lifetime for Credit Lines

• Exercise 62: Econometric model of credit line usage in R

• Exercise 63: EAD Lifetime model for individual credit line

• Exercise 64: EAD Lifetime Vintage Model for credit line pool in R and Excel

Module 26: Backtesting PD

• Exercise 65: Backtesting of PD in Excel

Module 27: LGD Backtesting

• Exercise 66: Comparison of the performance of the models using calibration and precision tests

Module 28: Backtesting EAD

• Exercise 67: Comparison of the performance of EAD models

Module 29: Backtesting Lifetime PD

• Exercise 68: Stability and accuracy test of Lifetime PD in Excel

• Exercise 69: Backtesting Lifetime PD over time

Module 30: Consumer Credit Risk Stress Testing

• Exercise 81: Stress Testing PD IRB in Excel and SAS multifactorial model Credit Portfolio View

• Exercise 70: Stress Testing PD IRB Multiyear Approach

• Exercise 71: Stress Test of the LGD econometric model in R

• Exercise 72: Transition Matrix Stress Test and Lifetime PD

• Exercise 73: Joint Stress Test of PD & LGD in SAS

(Nota: el orden de numeración de ejercicios en el texto original está mezclado; aquí lo he respetado tal cual.)

Module 31: Stress Testing Corporate Credit Risk

• Exercise 74: Stress Testing of corporate portfolio provisions using transition matrix and ASRF model in SAS, R and Excel

Module 32: Stress Testing of ECL IFRS 9

• Exercise 74 (second occurrence): ECL Stress Testing using matrices and time series in R and Excel

Module 33: Increased Risk SICR Credit

• Exercise 75: Estimation of SICR credit risk increase using ROC discriminant power test in R and Excel

Module 34: Lifetime Expected Credit Losses Models

• Global Exercise 76: Estimation of Lifetime Expected Loss Provisions of a consumer credit portfolio in R and Excel with VBA

Module 35: EL Validation

(No specific exercises listed.)

Module 36: Financial and capital impact by IFRS 9 endowments under COVID-19

(No specific exercises listed.)