Master Machine Learning, Neural Networks, and Generative Data to automate risk assessment and optimize compliance
Sub Category
- Business Analytics & Intelligence
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Objectives
- Preprocess complex financial data (e.g., German Credit Dataset) using R and tidyverse packages like dplyr to prepare for predictive modeling
- Architect and train Single-Layer and Multi-Layer Perceptrons (Neural Networks) using sigmoid and ReLU activation functions for financial prediction.
- Analyze the difference between exogenous and endogenous systemic risks, and understand how algorithmic trading can amplify prosyclical risk.
- Distinguish between RegTech and SupTech, evaluating how AI and ML are leveraged by financial supervisors and institutions for regulatory compliance.
- Implement Machine Learning models (SVMs, Decision Trees, Multivariate Regression) to accurately forecast credit ratings and default probabilities.
Pre Requisites
- Basic familiarity with programming concepts, preferably in R (as we utilize data frames, tibbles, and dplyr for data manipulation).
- A foundational understanding of basic statistics (mean, variance, linear combinations, and dummy variables) is helpful but not strictly required.
- No prior Machine Learning or Deep Learning experience is necessary; we build the architecture of Neural Networks and predictive models from the ground up.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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