Course Outline


  • Difference between statistical learning (statistical analysis) and machine learning
  • Adoption of machine learning technology and talent by finance companies

Understanding Different Types of Machine Learning

  • Supervised learning vs unsupervised learning
  • Iteration and evaluation
  • Bias-variance trade-off
  • Combining supervised and unsupervised learning (semi-supervised learning)

Understanding Machine Learning Languages and Toolsets

  • Open source vs proprietary systems and software
  • Python vs R vs Matlab
  • Libraries and frameworks

Understanding Neural Networks

Understanding Basic Concepts in Finance

  • Understanding Stocks Trading
  • Understanding Time Series Data
  • Understanding Financial Analyses

Machine Learning Case Studies in Finance

  • Signal Generation and Testing
  • Feature Engineering
  • Artificial Intelligence Algorithmic Trading
  • Quantitative Trade Predictions
  • Robo-Advisors for Portfolio Management
  • Risk Management and Fraud Detection
  • Insurance Underwriting

Introduction to R

  • Installing the RStudio IDE
  • Loading R Packages
  • Data Structures
  • Vectors
  • Factors
  • Lists
  • Data Frames
  • Matrices and Arrays

Importing Financial Data into R

  • Databases, Data Warehouses, and Streaming Data
  • Distributed Storage and Processing with Hadoop and Spark
  • Importing Data from a Database
  • Importing Data from Excel and CSV

Implementing Regression Analysis with R

  • Linear Regression
  • Generalizations and Nonlinearity

Evaluating the Performance of Machine Learning Algorithms

  • Cross-Validation and Resampling
  • Bootstrap Aggregation (Bagging)
  • Exercise

Developing an Algorithmic Trading Strategy with R

  • Setting Up Your Working Environment
  • Collecting and Examining Stock Data
  • Implementing a Trend Following Strategy

Backtesting Your Machine Learning Trading Strategy

  • Learning Backtesting Pitfalls
  • Components of Your Backtester
  • Implementing Your Simple Backtester

Improving Your Machine Learning Trading Strategy

  • KMeans
  • k-Nearest Neighbors (KNN)
  • Classification or Regression Trees
  • Genetic Algorithm
  • Working with Multi-Symbol Portfolios
  • Using a Risk Management Framework
  • Using Event-Driven Backtesting

Evaluating Your Machine Learning Trading Strategy's Performance

  • Using the Sharpe Ratio
  • Calculating a Maximum Drawdown
  • Using Compound Annual Growth Rate (CAGR)
  • Measuring Distribution of Returns
  • Using Trade-Level Metrics

Extending your Company's Capabilities

  • Developing Models in the Cloud
  • Using GPUs to Accelerate Deep Learning
  • Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion


  • Programming experience with any language
  • Basic familiarity with statistics and linear algebra
  28 Hours


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