ML Basics for Finance & Accounting Applications 

This course equips finance and accounting professionals with a solid foundation in the core concepts of Artificial Intelligence (AI) and machine learning (ML). You'll gain a practical understanding of how AI is transforming the financial landscape, explore key applications within finance and accounting, and learn the fundamentals of machine learning algorithms relevant to these domains. 

• Understand the fundamental principles of learning theory and its relevance to the Finance & Accounting Management

• Identify and quantify bias and variance in machine learning models

• Evaluate and optimize model performance using appropriate metrics and techniques

• Develop and deploy robust machine learning models for Finance & Accounting applications

1. Introduction to Learning Theory

• Overview of machine learning and its applications in the Finance & Accounting Management

• Fundamentals of learning theory, including the concept of learnability and the PAC learning framework

• The trade-off between model complexity and generalization

• Hands-on exercises: Implementing basic machine learning algorithms and evaluating their performance on Finance & Accounting datasets

2. Bias and Variance

• Understanding bias and variance in the context of model performance

• The bias-variance decomposition and its implications for model selection

• Techniques for estimating bias and variance, such as cross-validation and bootstrap

• Hands-on exercises: Quantifying bias and variance for various machine learning models using Finance & Accounting datasets

3. Regularization Techniques

• The concept of regularization and its role in mitigating overfitting

• L1 and L2 regularization methods (Lasso and Ridge regression)

• Elastic Net regularization and its advantages

• Hands-on exercises: Applying regularization techniques to improve model performance on Finance & Accounting case studies

4. Model Selection and Optimization

• Techniques for model selection, including grid search and random search

• Hyperparameter tuning and its impact on model performance

• Ensemble methods and their applications in the Finance & Accounting Management

• Hands-on exercises: Optimizing machine learning models for real-world Finance & Accounting problems

• Basic understanding of mathematics, including calculus and linear algebra

• Familiarity with programming concepts and a language such as Python

• Knowledge of basic machine learning concepts and algorithms