Gain a thorough understanding of machine learning concepts and their critical role in financial applications, specifically asset pricing. Develop the ability to build, evaluate, and enhance regression models. Acquire hands-on experience with Python and R for implementing machine learning algorithms. Learn to apply advanced techniques like decision trees, random forests, and neural networks to predict stock returns. Master model optimization, tuning, and validation to ensure robust and reliable financial predictions. Explore real-world case studies to bridge theoretical knowledge with practical financial scenarios.

Introduction to Machine Learning and Financial Applications

• ML Basics: In a Nutshell
• What is ML?
• Data: Introduction and Importance in Finance

Fundamentals of Supervised Regression

• Supervised Regression: In a Nutshell
• Linear Models with L2 Loss
• The Use of Linear Regression in Finance
• Linear Models with L1 Loss
• Introduction to Regularization: Ridge and Lasso Regression

Performance Evaluation in Regression Models

• Evaluation: In a Nutshell
• Measures for Regression
• Overfitting & Underfitting
• Resampling Techniques

Decision Trees and Ensemble Methods

• CART: In a Nutshell
• Splitting Criteria for Regression
• Stopping Criteria & Pruning

Random Forests

• Random Forests: In a Nutshell
• Bagging Ensembles
• Out-of-Bag Error Estimate

Model Tuning and Evaluation

• Tuning: In a Nutshell
• Problem Definition
• Basic and Advanced Techniques

Introduction to Deep Learning with Neural Networks

• Neural Networks: In a Nutshell, Introduction, Single Neuron
• Single Hidden Layer Neural Network
• Multi-Layer Feedforward Neural Networks (MLP)
• Neural Network Architectures and Optimization Techniques
• Optimization
• Basic Backpropagation

Practical Applications and Case Studies in Finance

• Practical Session: Comprehensive Case Study on Predicting Stock Returns Using ML Techniques
• Finance Focus: Real-World Applications, Challenges, and Future Directions