这是一个证书 我不考 但我可以遵循他的大纲
The financial industry has been adopting AI and machine learning at a rapid pace. Alternative datasets including text analytics, cloud computing, and algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling models. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages, and data sets that are entering the enterprise. With little formal guidance from regulators on how to validate models and quantify model risk, organizations are developing their own home-cooked methods to address model risk management challenges.
In this course, we aim to bring clarity to some of the model risk management and validation challenges with data science and machine learning models in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model risk management is changing. We will introduce key concepts and discuss aspects to be considered when developing a model risk management framework incorporating data science techniques and machine learning methodologies in a pragmatic way.
Learning Objectives
Upon completion of this course, you will be able to:
- Describe the role of Machine Learning and AI in financial services
- Discuss Model Risk Management challenges and best practices for machine learning models
- Validate machine learning models: Quantifying risk, best practices and templates
- Understand the regulatory guidance and the future
- Experience practical case studies with sample code
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