Course Outline
Introduction
- 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
Requirements
- Programming experience with any language
- Basic familiarity with statistics and linear algebra
Testimonials (4)
Personal service and orientated to my needs
ANN - New Vitality Clinic
Course - GnuCash for Business Accounting
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
The lecturer is very knowledgeable and can substantiate theories with his own personal experiences.
Harry Estipona
Course - Financial Markets
I was benefit from the interesting and clear ideas and suggestions.