Machine Learning in Finance

By attending Machine Learning in Finance workshop, Participants will:

  • Practitioners working at financial institutions such as banks, asset management firms or hedge funds
  • Individuals interested in applications of ML for personal day trading
  • Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance 

This Machine Learning in Finance training course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.

The goal  of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

COURSE AGENDA

  • Specialization Objectives
  • Specialization Prerequisites
  • Artificial Intelligence and Machine Learning, Part I
  • Artificial Intelligence and Machine Learning, Part II
  • Machine Learning as a Foundation of Artificial Intelligence, Part I
  • Machine Learning as a Foundation of Artificial Intelligence, Part II
  • Machine Learning as a Foundation of Artificial Intelligence, Part III
  • Machine Learning in Finance vs Machine Learning in Tech, Part I
  • Machine Learning in Finance vs Machine Learning in Tech, Part II
  • Machine Learning in Finance vs Machine Learning in Tech, Part III
  • Generalization and a Bias-Variance Tradeoff
  • The No Free Lunch Theorem
  • Overfitting and Model Capacity
  • Linear Regression
  • Regularization, Validation Set, and Hyper-parameters
  • Overview of the Supervised Machine Learning in Finance
  • DataFlow and TensorFlow
  • A First Demo of TensorFlow
  • Linear Regression in TensorFlow
  • Neural Networks
  • Gradient Descent Optimization
  • Gradient Descent for Neural Networks
  • Stochastic Gradient Descent
  • Regression and Equity Analysis
  • Fundamental Analysis
  • Machine Learning as Model Estimation
  • Maximum Likelihood Estimation
  • Probabilistic Classification Models
  • Logistic Regression for Modeling Bank Failures, Part I
  • Logistic Regression for Modeling Bank Failures, Part II
  • Logistic Regression for Modeling Bank Failures, Part III
  • Supervised Learning: Conclusion

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