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machine-learning

  1. Nicole Seaman

    P2.T9.905. Machine learning in financial services: use cases and possible effects (FSB FIN, part 2 of 2)

    Learning Objectives: Describe the use of AI and machine learning in the following cases: operations focused uses; trading and portfolio management in financial markets; and uses for regulatory. Describe the possible effects and potential benefits and risks of AI and machine learning on financial...
  2. Nicole Seaman

    P2.T9.904. Artificial intelligence and machine learning (AI & ML) in financial services (FSB FIN, part 1 of 2)

    Learning objectives: Describe the drivers that have contributed to the growing use of Fintech and the supply and demand factors that have spurred adoption of AI and machine learning in financial services. Describe the use of AI and machine learning in the following cases: customer-focused uses...
  3. Nicole Seaman

    P2.T9.902. Big data techniques including machine learning (Varian)

    Learning objectives: Describe the issues unique to big datasets. Explain and assess different tools and techniques for manipulating and analyzing big data. Examine the areas for collaboration between econometrics and machine learning Questions: 902.1. About the analysis of big data, Hal Varian...
  4. Nicole Seaman

    P2.T9.803. Machine learning (van Liebergen)

    Learning objectives: Describe the process of machine learning and compare machine learning approaches. Describe the application of machine learning approaches within the financial services sector and the types of problems to which they can be applied. Analyze the application of machine learning...
  5. Nicole Seaman

    P2.T9.802. Big Data: New Tricks for Econometrics by Hal Varian

    Learning objectives: Describe the issues unique to big datasets. Explain and assess different tools and techniques for manipulating and analyzing big data. Examine the areas for collaboration between econometrics and machine learning. Questions: 802.1. Below is Hal Varian's simple...
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