IFRS 9 ​ Module 0: Implementation of IFRS 9 in the EU ​ SICR assessment approaches Approaches for determining stage transfers Alignment between the Definition of Default and IFRS 9 exposures in Stage 3 Low credit risk exemption 12-month PD as proxy for lifetime PD Expected Credit Loss Models Types of Expected Credit Loss models Model limitations and use of overlays Effects from the Russian/Ukrainian conflict ESG including climate risks IFRS 9 PD variability and robustness Variability in the IFRS 9 PD Differences in the use of IRB models for IFRS 9 estimates Differences in the definition of default Differences in risk differentiation Differences in risk quantification Treatment of 2020-2021 defaults Incorporation of forward-looking information Macroeconomic scenarios Variability of the methodological approach for incorporation of FLI and reflection of non-linearity Incorporation of FLI at parameter level List of macroeconomic variables used for FLI incorporation Forecasting period and reversion to long-term average Variability in the impact and different sensitivities from FLI Effect of non-linearity and probability framework Focus on backtesting practices Staging allocation ECL measurement IFRS 9 LGD estimates IFRS 9 PD estimates Overlays Forward-looking information ​CREDIT SCORING ​ Module 1: Exploratory Analysis ​ Exploratory Data Analysis EDA Data sources Data review Target definition Time horizon of the target variable Sampling Random Sampling Stratified Sampling Rebalanced Sampling Exploratory Analysis: histograms Q Q Plot Moment analysis boxplot Treatment of Missing values Multivariate Imputation Model Advanced Outlier detection and treatment techniques Univariate technique: winsorized and trimming Multivariate Technique: Mahalanobis Distance Over and Undersampling Techniques ​Random oversampling Synthetic minority oversampling technique (SMOTE) ​ Module 2: Feature engineering ​ Feature engineering Data Standardization Variable categorization Equal Interval Binning Equal Frequency Binning Chi-Square Test Binary coding WOE Coding WOE Definition Univariate Analysis with Target variable Variable Selection Treatment of Continuous Variables Treatment of Categorical Variables Information Value Optimization of continuous variables Optimization of categorical variables ​Exercise 1: EDA Exploratory Analysis ​Exercise 2: Feauture 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 ​ Machine Learning ​ Module 3: Machine Learning ​ Unsupervised models​ K Means Principal Component Analysis (PCA) Advanced PCA Visualization​​ Supervised Models Ensemble Learning Bagging trees Random Forest Boosting Adaboost Gradient Boosting Trees 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 ​ DEEP LEARNING ​ Module 4: Deep Learning ​ Feed Forward Neural Networks Single Layer Perceptron Multiple Layer Perceptron Neural network architectures Activation function​ Back propagation Directional derivatives gradients Jacobians Chain rule Optimization and local and global minima Deep Learning Convolutional Neural Networks CNN​​ CNN for pictures Design and architectures convolution operation filters strider padding Subsampling pooling fully connected Credit Scoring using CNN Recent CNN studies applied to credit risk and scoring Deep Learning Recurrent Neural Networks RNN​​ Long Term Short Term Memory (LSTM) Hopfield Bidirectional associative memory descending gradient Global optimization methods RNN and LSTM for credit scoring One-way and two-way models​​ Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) Fundamental components of the GANs GAN architectures Bidirectional GAN Training generative models Credit Scoring using GANs 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 ​ Hyperparameterization Grid search Random search Bayesian Optimization Train test split ratio Learning rate in optimization algorithms (e.g. gradient descent) Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer) Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh) Selection of loss, cost and custom function Number of hidden layers in an NN Number of activation units in each layer The drop-out rate in nn (dropout probability) Number of iterations (epochs) in training a nn Number of clusters in a clustering task Kernel or filter size in convolutional layers Pooling size Batch size Interpretation of the Shap model 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 Scoring assignment Scorecard Classification Scorecard WOE Binary Scorecard Continuous Scorecard Scorecard Rescaling Factor and Offset Analysis Scorecard WOE Binary Scorecard Reject Inference Techniques cut-off parceling Fuzzy Augmentation Machine Learning Advanced Cut Point Techniques Cut-off optimization using ROC curves Exercise 20: Creating a scorecard using Excel, R, and Python ​ QUANTUM COMPUTING Module 7: Quantum Computing and Algorithms ​ Future of quantum computing in banking Is it necessary to know quantum mechanics? QIS Hardware and Apps Quantum operations Qubit representation Measurement Overlap Matrix multiplication Qubit operations Multiple Quantum Circuits Entanglement Deutsch Algorithm Quantum Fourier transform and search algorithms Hybrid quantum-classical algorithms Quantum annealing, simulation and optimization of algorithms Quantum machine learning algorithms Exercise 21: Quantum operations QUANTUM MACHINE LEARNING ​ Module 8: Development of Credit Scoring using Quantum Machine Learning ​ What is quantum machine learning? Qubit and Quantum States Quantum Automatic Machine Algorithms Quantum circuits K means quantum Support Vector Machine Support Vector Quantum Machine Variational quantum classifier Training quantum machine learning models Quantum Neural Networks Quantum GAN Quantum Boltzmann machines Quantum machine learning in Credit Risk Quantum machine learning in credit scoring Quantum software 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 What are tensor networks? Quantum Entanglement Tensor networks in machine learning Tensor networks in unsupervised models Tensor networks in SVM Tensor networks in NN NN tensioning Application of tensor networks in credit scoring models Exercise 25: Construction of credit scoring and PD using tensor networks ​ PROBABILISTIC MACHINE LEARNING ​ ​Module 10: Probabilistic Machine Learning ​​ Probability Gaussian models Bayesian Statistics Bayesian logistic regression Kernel family Gaussian processes Gaussian processes for regression Hidden Markov Model Markov chain Monte Carlo (MCMC) Metropolis Hastings algorithm Machine Learning Probabilistic Model Bayesian Boosting Bayesian Neural Networks Exercise 26: Gaussian process for regression Exercise 27: Bayesian neural networks ​ ADVANCED VALIDATION Module 11: Advanced Validation of AI Models Integration of state-of-the-art methods in interpretable machine learning and model diagnosis. Data Pipeline Feature Selection Black-box Models Post-hoc Explainability Global Explainability Local Explainability Model Interpretability Diagnosis: Accuracy, WeakSpot, Overfit, Reliability, Robustness, Resilience, Fairness Model comparison Comparative for Regression and Classification Fairness Comparison Exercise 28: Validation and diagnosis of advanced credit scoring models