Guest Lecturer for Machine Learning

Academic Teaching & Knowledge Transfer

Developed and delivered a comprehensive machine learning curriculum focused on healthcare applications, transferring industry expertise to students.

Academic Contributions

Curriculum Development

• Teaching data transformation principles and the data value chain
• Fundamentals of machine learning, including Random Forest and Support Vector Machines
• Introduction to Neural Networks and Deep Learning architectures
• Convolutional Neural Networks and Recurrent Neural Networks for image processing and sequence analysis

Advanced Topics & Practical Applications

• Big Data concepts: MapReduce paradigm and Data Warehouse architectures
• Practical applications with visualization and prototyping in Jupyter Notebooks
• Real-world case studies from healthcare data analytics
• Integration of theoretical concepts with practical implementation

Ethics & Professional Standards

• Ethical aspects and data protection in the application of AI in healthcare
• GDPR compliance in data science projects
• Responsible AI development and deployment practices
• Industry best practices for healthcare data handling

Professional Recognition

Academic Opportunity

• Received an offer for Full Professorship (W2) in Data Science in Healthcare
• Opportunity declined to pursue industry leadership roles
• Recognition of expertise in both academic and practical applications

This teaching experience allowed me to formalize my knowledge while contributing to the academic community and preparing students for careers in data science and healthcare technology.