Course Outline
Foundamentals of Machine Learning in Finance
- Landscape of AI and ML applications within the financial sector
- Categorization of machine learning methods (supervised, unsupervised, and reinforcement learning)
- Practical case studies involving fraud detection, credit scoring, and risk modeling
Python Fundamentals and Data Management
- Leveraging Python for data processing and analytical tasks
- Analyzing financial datasets using Pandas and NumPy libraries
- Visualizing data insights with Matplotlib and Seaborn
Supervised Learning for Financial Forecasts
- Implementation of linear and logistic regression techniques
- Application of decision trees and random forest algorithms
- Assessing model efficacy through accuracy, precision, recall, and AUC metrics
Unsupervised Learning and Anomaly Identification
- Application of clustering methods (K-means, DBSCAN)
- Utilization of Principal Component Analysis (PCA)
- Detecting outliers for enhanced fraud prevention strategies
Credit Scoring and Risk Modeling
- Constructing credit scoring models via logistic regression and tree-based methods
- Managing imbalanced datasets in risk assessment scenarios
- Ensuring model interpretability and fairness in financial decisions
Machine Learning for Fraud Detection
- Identification of prevalent forms of financial fraud
- Applying classification algorithms for anomaly recognition
- Strategies for real-time scoring and model deployment
Model Deployment and Ethical AI in Finance
- Deploying models using Python, Flask, or cloud-based solutions
- Navigating ethical considerations and regulatory standards (e.g., GDPR, explainability)
- Monitoring and retraining models within production environments
Conclusion and Future Directions
Requirements
- Proficiency in foundational statistics and core financial principles
- Practical experience with Excel or similar data analysis platforms
- Foundational programming skills, with a preference for Python
Target Audience
- Financial analysts
- Actuaries
- Risk management officers
Testimonials (5)
Possible applications /exercises
Estelle De la Fouchardiere - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I really enjoyed seeing how using this tool can really improve and automate work. I also appreciated the initial part where we were helped to eliminate our prejudice against artificial intelligence. The examples are wonderful.
chiara di egidio - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I liked to get knowledge about new possibilities
Maciej Karolczak - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I like the examples, so we have an idea of what is possible
Deborah Highes
Course - Machine Learning & AI for Finance Professionals
it has opened my mind to new tool that can help me in creating automation
