Module 1: Guidelines on PD, LGD & Defaulted Exposures Topics Reduction of parameter variability Homogenization of PD/LGD calculation Data quality and representativeness Human judgment & margin of conservatism (MoC) PD estimation (development, data, drivers, philosophy) PD calibration (1-year DR, observed DR, LRA DR) LGD estimation (recoveries, collaterals, costs, calibration) LGD in-default & ELBE Application of risk parameters Review, documentation, impact assessment (No exercises listed) Module 2: Implementation of IFRS 9 in the EU Topics SICR & staging Low credit risk exemption PD as proxy for lifetime ECL model types Model limitations & overlays ESG & climate risk influence IFRS 9 PD variability FLI incorporation (macroeconomic scenarios, non-linearity) Staging, measurement, overlays IFRS 9 LGD, IFRS 9 PD (No exercises listed) Module 3: Exploratory Analysis Topics Data sources, target definition Sampling (random, stratified, rebalanced) EDA: histograms, QQ plot, boxplot Missing values & multivariate imputation Outliers: winsorizing, trimming, Mahalanobis Oversampling / SMOTE Exercises EDA Exploratory Analysis Feature Engineering Detection & treatment of Advanced Outliers Multivariate imputation of missing values Univariate analysis in percentiles (R) Optimal univariate continuous-variable analysis (Excel) Module 4: Feature Engineering Topics Standardization Binning (equal interval, frequency, chi-square) WOE coding Variable selection Continuous & categorical optimization Information Value (Exercises listed in Module 3) Module 5: Machine Learning Topics K-Means PCA & visualization Ensemble Learning: Bagging, Random Forest, Boosting, AdaBoost, Gradient Boosting Exercises Segmentation using K-Means Credit Scoring SVM Credit Scoring Boosting Credit Scoring Bagging Credit Scoring Random Forest (R & Python) Credit Scoring Gradient Boosting Trees Module 6: Deep Learning Topics Feedforward NN, MLP Activation functions Backpropagation, gradients, Jacobians CNNs (filters, pooling, FC layers) CNN for credit scoring RNN, LSTM GANs for credit scoring Exercises Deep Learning Feedforward Credit Scoring CNN for Credit Scoring LSTM Credit Scoring GANs Credit Scoring Module 7: Hyperparameter Tuning Topics Grid, random & Bayesian search Learning rate, optimization algorithms Activation functions Loss & cost functions Hidden layers, units, epochs Dropout, batch size SHAP interpretation Exercises Tuning XGBoost, Random Forest & SVM Tuning Deep Learning models Module 8: Scorecard Development Topics Scorecard design (binary, continuous) WOE classification Reject inference Cut-off optimization (ROC) Exercises Scorecard creation in Excel, R, Python Module 9: Quantum Computing & Algorithms Topics Qubits, circuits, measurement Quantum operations & entanglement Deutsch algorithm QFT Hybrid quantum-classical algorithms Quantum ML Exercises Quantum operations Module 10: Quantum Machine Learning for Credit Scoring Topics QSVM VQC Quantum NN, QGAN Quantum ML for PD & scoring Exercises Quantum SVM Credit Scoring Quantum FFNN for PD Quantum CNN for PD Module 11: Tensor Networks Topics Entanglement Tensor networks for ML Applications in NN & SVM Credit scoring using tensors Exercises Credit Scoring & PD with Tensor Networks Module 12: Probabilistic Machine Learning Topics Bayesian models Gaussian processes HMM MCMC Bayesian NN Exercises Gaussian Process Regression Bayesian Neural Networks Module 14: Advanced Validation of AI Models Topics Explainability, SHAP Global & local explanations Model diagnosis (accuracy, robustness, fairness) Regression & classification comparison Exercises Validation & Diagnosis of Advanced Scoring Models Module 15: IRB PD – Probability of Default Topics PD estimation Data requirements PD calibration Technical defaults PD PIT & TTC ML & DL PD Margin of conservatism Exercises Modeling Margin of Conservatism (PD) Module 16: Econometric & AI PD Models Topics Logistic, Probit, Cox, Log-Log Panel Models Bayesian logistic ML survival models Random Survival Forest Cox-XGBoost Deep Learning Survival Quantum NN Exercises Cox Regression PD Panel Logistic PD Bayesian Logistic Regression LASSO PD Random Forest Survival Cox-XGBoost Deep Learning Survival Feedforward NN Quantum NN PD Module 17: PD Calibration IRB Topics Central tendency Bayesian calibration Scaled calibration Smoothing PD curves QMM, Beta, Laplace, isotonic regression Gaussian Process Regression Exercises Platt Scaling & Isotonic Regression Gaussian Process Regression Calibration Asymmetric Laplace Calibration Module 18: Bayesian PD & Gaussian Processes Topics Bayesian PD estimation MCMC Credibility intervals Gaussian process regression Exercises Bayesian Logistic PD (Python) PD using MCMC (R) Gaussian Process Regression PD Module 19: Low-Default Portfolio PD Topics PD in LDP Bayesian neutral & conservative PD GAN synthetic data for LDP Exercises LDP PD – Confidence Interval Multi-period CI PD Neutral Bayesian PD Conservative Bayesian PD Synthetic Data for PD via GAN Module 20: IFRS 9 PD Forecasting Topics PIT PD TTC PD ARIMA, VAR, VARMAX LSTM, Bayesian LSTM Quantum LSTM Exercises PD Forecasting with VARMAX PD Forecasting with Quantum LSTM Module 21: Lifetime PD Topics Vintage & EMV models Regression & survival models Markov models ML & DL Exercises Multinomial Regression – Lifetime PD Multi-State Markov – Lifetime PD Matrix ASRF – Lifetime PD Quantum SVM – Lifetime PD SVM – Lifetime PD Deep Learning Lifetime PD Quantum Deep Learning Lifetime PD Module 22: IRB LGD (Retail) (No exercises) Module 23: Econometric & ML Models for LGD Exercises Econometric LGD Models ML & Deep Learning LGD Model Comparison Tests Module 24: LGD IFRS 9 Exercises RF LGD IFRS 9 Beta Regression + NN LGD IFRS 9 XGBoost LGD IFRS 9 SVM LGD IFRS 9 Quantum SVM LGD IFRS 9 Traditional NN LGD Bayesian NN LGD Module 25: IRB EAD & CCF Exercises NN CCF Model SVR CCF (Python) NN + Beta Regression (R) Quantum SVM CCF Module 26: IFRS 9 EAD – Contractual Options Exercises Prepayment Model (RF, R & Excel) Module 27: Lifetime EAD for Credit Lines Exercises NN Model for Lines of Credit Lifetime EAD Model Module 28: PD Backtesting Exercises PD Backtesting in Excel Module 29: LGD Backtesting Exercises LGD Model Performance Comparison Module 30: EAD Backtesting Exercises EAD Model Performance Comparison Module 31: Deep Learning for Macroeconomic Dynamics Exercises Neural Networks for Bellman Equation Module 32: Deep Learning for Macroeconomic Projections Exercises Charge-off Model with VAR & VEC PD Forecasting with Bayesian LSTM Pandemic Forecasting with RNN LSTM PD Forecasting with Quantum NN Module 33: Stress Testing PD & LGD Exercises Credit Portfolio View – PD Stress Test Bayesian LSTM PD Stress Test VQR Quantum Regression PD Stress Test MARS Stress Test PD LASSO Stress Test Module 34: Corporate Portfolio Stress Testing Exercises Corporate PD Stress Test (ASRF + Transition Matrix) Module 35: ECL IFRS 9 Stress Testing Exercises Internal ECL Stress Test (R & Excel) Module 36: Quantum Stress Testing Exercises DGSE Model with Deep Learning Quantum vs Classical Monte Carlo Module 37: Economic Capital Models Exercises Portfolio EL/UL/Correlation/EC CreditRisk+ in SAS CreditMetrics in Excel & R Single-Factor EC Model Module 38: Quantum EC Models Exercises Quantum EL, VaR & ES Module 39: Climate Risk in Credit Risk Exercises PD & DD for Transition Risk PD & DD for Physical Risk

Module 1: Economic Capital (EC) (No exercises) Module 2: Economic Capital Regulation (No exercises) Module 3: Available Capital vs. Economic Capital (No exercises) Module 4: Regulatory Capital IRB vs. Economic Capital (No exercises) Module 5: Asset Correlation & Default Correlation Exercise 1: Asset correlation using financial time series Exercise 2: Asset correlation using default time series Exercise 3: Intercorrelation & intracorrelation Exercise 4: Calibration of asset correlation & default correlation Exercise 5: Correlation estimation using MCMC simulation Module 6: Structural Models for Economic Capital Exercise 6: Estimation of Distance to Default Exercise 7: Merton model simulation, VaR & ES estimation Module 7: Economic Capital – Covariance Approach Exercise 8: Unexpected loss & economic capital – corporate portfolio Exercise 9: Unexpected loss & economic capital – retail portfolio Module 8: Economic Capital – ASRF Model Exercise 10: ASRF analytical solution & Monte Carlo simulation Exercise 11: Multifactor t-student model using Monte Carlo Module 9: Multifactor Economic Capital Model Exercise 12: Multifactor model using Monte Carlo simulation Module 10: CreditMetrics for Economic Capital Exercise 14: CreditMetrics model in Excel Exercise 15: CreditMetrics using Monte Carlo simulation in Python Module 11: Econometric Models for Economic Capital Exercise 16: Credit Portfolio Views in SAS & Excel Exercise 17: Multifactor model with macroeconomic variables in R Module 12: CreditRisk+ for Economic Capital Exercise 18: CreditRisk+ single-sector retail Exercise 19: CreditRisk+ sector model – analytical solution Exercise 20: CreditRisk+ sector model – Monte Carlo simulation Exercise 21: CreditRisk+ with copula dependence Exercise 22: CreditRisk+ single-sector with FFT Exercise 23: Marginal risk contribution in CreditRisk+ Module 14: Mixture Models for Economic Capital Exercise 24: EC Logit–Normal Exercise 25: EC Probit–Normal Exercise 26: EC Beta–Binomial Exercise 27: EC Poisson–Gamma Module 15: Dependency Modeling with Copulas Exercise 28: Gaussian & t-copula estimation Exercise 29: EC using Gaussian & t-copulas Exercise 30: EC using Frank & Clayton copulas Module 16: Concentration Risk Exercise 31: EC with ASRF + granularity adjustment Exercise 32: CDI, Beta & DF calibration Module 17: Capital Allocation & Planning Exercise 33: Capital allocation using ASRF; VaR & ES estimation Module 18: Limits for Economic Capital & Concentration Exercise 34: Individual & sector economic capital limits Module 19: Profitability Measures & Strategic Planning Exercise 35: Hurdle Rate, RAROC & RORWA estimation Module 20: Optimization of Economic & Regulatory Capital Exercise 36: Scenario creation Exercise 37: Stochastic dynamic programming model (multiperiod) for optimizing financial margin under leverage, NSFR, LCR, regulatory capital & EC constraints Module 21: Credit Risk Appetite Exercise 38: Credit Risk Appetite framework