fundamentals of machine learning in finance

By attending Fundamentals of Machine Learning in Finance workshop, Participants will: 

  • 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

The Fundamentals of Machine Learning in Finance training course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: 

  1. understanding where the problem one faces lands on a general landscape of available ML methods, 
  2. understanding which particular ML approach(es) would be most appropriate for resolving the problem, and 
  3. ability to successfully implement a solution, and assess its performance.  

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.

Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

COURSE AGENDA

  • SM. Latent Variables
  • Sequence Modeling
  • SM. Latent Variables for Sequences
  • SM. State-Space Models
  • SM. Hidden Markov Models
  • Neural Architecture for Sequential Data
  • RL. Introduction
  • RL. Core Ideas
  • Markov Decision Process and RL
  • RL. Bellman Equation
  • RL and Inverse Reinforcement Learning
  • UL. Clustering Algorithms
  • UL. K-clustering
  • UL. K-means Neural Algorithm
  • UL. Hierarchical Clustering Algorithms
  • UL. Clustering and Estimation of Equity Correlation Matrix
  • UL. Minimum Spanning Trees, Kruskal Algorithm
  • UL. Probabilistic Clustering
  • Core Concepts of UL
  • PCA for Stock Returns, Part 1
  • PCA for Stock Returns, Part 2
  • Dimension Reduction with PCA
  • Dimension Reduction with tSNE
  • Dimension Reduction with Autoencoders
  • Practitioners What is Machine Learning in Finance?
  • Introduction to Fundamentals of Machine Learning in Finance
  • Support Vector Machines, Part 1
  • Support Vector Machines, Part 2
  • SVM. The Kernel Trick
  • Example: SVM for Prediction of Credit Spreads
  • Tree Methods. CART Trees
  • Tree Methods: Random Forests
  • Tree Methods: Boosting

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