Model Predictive Control of Nonlinear Systems with Deep Learning
A comprehensive master thesis project focused on the application of deep learning techniques for model predictive control of nonlinear systems, including problem definition, data collection, model development, evaluation, and deployment.
My master thesis is focused on the development of a comprehensive data science project that encompasses the entire workflow of a data science project, from problem definition and data collection to feature engineering, model development, evaluation, and deployment. The project is designed to demonstrate my ability to apply data science techniques to real-world problems, showcasing my proficiency in Python, SQL, machine learning libraries, and deployment tools like Streamlit. The thesis will be structured into several sections, each detailing a specific aspect of the project, including the problem description, exploratory data analysis, model development and evaluation, and model deployment with Streamlit. Through this thesis, I aim to provide a comprehensive solution to a real-world data science problem while demonstrating my technical skills and understanding of the data science workflow.