About this Course

This course is designed to help architects and architectural students harness the power and potential of Artificial Intelligence in their practices. It explores the foundational sciences behind AI technologies, such as machine learning and computer vision, and integrates these with design thinking and methodology. Through this course, learners will acquire up-to-date skills that align with the demands of the evolving roles in design, thereby making them more competent and versatile in the workforce.

What Students Will Learn

• Understanding of machine learning as fundamental to AI technology.
• Insights into computer vision and its integration into AI for enhancement of architectural design.
• Skills to identify and leverage data pertinent to building environments for informed design decisions.
• Ability to re-conceptualize design tasks as scientific investigations.
• Proficiency in Python programming for practical application of machine learning in projects.

Prerequisites

No prerequisites are required, making this course accessible to anyone interested in integrating AI with architectural design.

Course Coverage

• Introduction to machine learning and AI fundamentals.
• In-depth exploration of computer vision.
• Adoption of algorithmic and data-driven thinking.
• Hands-on Python projects interfacing with machine learning.
• Statistical machine learning applications for empirical design validation.

Target Audience

This course is tailored for architects and architectural students, but it can also benefit designers from other disciplines seeking to expand their knowledge and skills in AI integration in design.

Application of Skills in the Real World

Skills gained from this course can help professionals in architecture and design fields use AI to enhance creativity, improve design efficiency, and solve complex design challenges innovatively. Additionally, these skills offer broader career opportunities in areas involving technology-driven design solutions.

Syllabus Overview

Module 1: Understanding AI

• Fundamentals of data, information, and knowledge.
• Core concepts in AI, machine learning, and computer vision.
• Overview of deep learning frameworks and learning techniques.

Module 2: Comprehension - Machine Learning for Design Problems

• Comparison between algorithmic thinking and data-driven approaches.
• Methods for validating architectural quality with data.

Module 3: Application - The Design Question

• Techniques to redefine a design question using AI.
• Approaches to defining real-world problems employing different methodologies.

Module 4: Analysis - Python Programming for the Design Question

• Utilization of Python programming and relevant libraries to manage design-based data.
• Development of practical skills for approaching data-driven design queries.