Navarro-Mateu, D ‘An exercise on the evolving city’, Dec 2021
Learning will be devoted to the understanding of an up-to-date status of Narrow AI techniques. We will be devoted to provoking the understanding of the different types of problems weak AI domains tackle and their applicability within AEC. Special focus will be made on the capabilities of AI for analysis, design, planning, and management.
The sessions are grouped into approaches exploring different automation paradigms:
- Statistical Learning + Optimization:
- Markov Models + Uncertainty Management
- Linear & logistic regression
- Evolutionary Computation
- Planning & approximate reasoning
- Machine Learning:
- Unsupervised processes & clustering
- PCA, SOM + HBI
- Supervised Decision Making:
- ANNs, Lazy learning, Deep learning, GAN
The module’s outcomes are not just theoretical knowledge; they also focus on students’ analytical abilities through various case studies examined during the sessions. It is presented as a framework for developing methodologies to tackle unresolved AEC scenarios.
Learning Objectives
The course develops students’ critical thinking abilities, engaging them in debates that question different approaches and outcomes. Students critically reflect on a proposed framework, assessing the efficiency and suitability of AI methodologies for solving specific, existing AEC problems.
The course emphasizes developing short investigations that are both reflective and practical, producing conclusive outputs and fostering innovative visions for AI application in the AEC industry.
