Advanced Machine Learning in Finance

Machine learning has revolutionized the financial industry, bringing new opportunities for analysis and decision-making. In this course, we delve into the advanced concepts and techniques of machine learning as applied to finance.

Course Outline

• Introduction to Machine Learning in Finance

  • Overview of machine learning and its applications in finance
  • Importance of data and data preprocessing

• Supervised Learning Techniques

  • Linear regression and logistic regression
  • Decision trees and random forests
  • Support vector machines

• Unsupervised Learning Techniques

  • Clustering methods (K-means, hierarchical clustering)
  • Dimensionality reduction techniques (PCA, t-SNE)

• Reinforcement Learning in Finance

  • Q-learning and policy gradients
  • Trading algorithms and reinforcement learning

• Advanced Topics

  • Deep learning and neural networks
  • Natural language processing for financial text analysis
  • Time series forecasting and predictive analytics

Course Materials

• Textbooks

  • "Machine Learning for Dummies" by John Paul Mueller
  • "Deep Learning for Finance" by Adam L. J. Nikitin

• Online Resources

  • Kaggle for datasets and competitions
  • Coursera for online courses

Learning Outcomes

• Understand the fundamentals of machine learning and its applications in finance
• Apply various machine learning techniques to real-world financial problems
• Develop predictive models and algorithms for financial analysis

Machine Learning in Finance

For more information on machine learning in finance, visit our Machine Learning Basics course.