Summer School ARTISTE 2025 Navigation Menu
Expected Audience
Master students, PhD candidates, post-doctoral fellows, Researchers, and Passionates and Practitioners.
The course will be offered in English.
Participants should bring a laptop with an up-to-date Python environment (Anaconda or equivalent) and the necessary libraries (NumPy, Pandas, Matplotlib, SciPy, Keras, PyTorch, etc.). To avoid issues in setting the environment, the course will employ Google Colab.
If you are a PoliTO M.Sc. Student in Civil Engineering program, click here for additional information!
If you are a PoliTO Ph.D. Student, click here click here for additional information!
Prerequisites
Basic understanding of mathematics and statistics. Familiarity with programming concepts. Prior knowledge of Python, including packages such as Pandas, NumPy, Matplotlib, and Seaborn, is recommended. Alternatively, participants can complete a preliminary Python workshop or review the provided materials before the course begins.
This three-day intensive program is designed for civil engineering and architecture students initiating their research. The course aims to provide a solid foundation in machine learning concepts and practical Python programming skills necessary for tackling complex, data-driven challenges in these domains. Participants will learn to:
- Understand the core principles of machine learning and how they apply to civil engineering and architecture.
- Implement machine learning algorithms in Python using popular libraries such as SciPy, Keras, and PyTorch.
- Analyze and interpret results derived from real-world datasets pertinent to Civil Engineering and architecture.
The course combines theoretical lectures with hands-on, guided exercises to ensure students not only grasp key concepts but also gain practical experience they can immediately apply to their research. The curriculum is designed to help participants confidently integrate machine learning methods into their doctoral work, driving innovation and solutions across a wide range of challenges in the built environment.
Learning Outcomes
By the end of this course, participants will be equipped with the knowledge and hands-on experience to confidently implement machine learning techniques in their research and professional projects.
Lecture slides and notes. Jupiter notebooks with code examples. Access to sample datasets related to civil engineering and architecture.
Participants will:
- Establish a solid foundation in Machine Learning principles
- Gain hands-on experience using Google Colab
- Master Python tools such as PyTorch, TensorFlow, and Scipy
- Investigate Symbolic Regression and Computational Intelligence
- Enhance model optimization skills
- Apply advanced techniques to research in Civil Engineering and Architecture
