# Optimizing Design Decisions With Machine Learning

> Nowadays, computers are often the ones calling the shots, and this trend is everywhere. From creative/aesthetic decisions to preparing blueprints, you’re always trying to make the best choice based on a few key details. But making the right call quickly isn’t easy be…

## At a glance

- Format: On-demand course
- Price: €105.00 (list €125.00)
- Difficulty: Advanced
- Duration: 8 Hours
- Schedule: May 25-26, 2024
- Instructors: Zvonko Vugreshek
- Categories: Architectural Design, Design Workshops
- Software: Grasshopper 3D, Rhinoceros 3D, LunchBox, Owl, Octopus
- Students: 67
- Rating: 4.3 / 5
- Canonical: https://paacademy.com/course/optimizing-design-decisions-with-machine-learning
- Enroll: https://paacademy.com/course/optimizing-design-decisions-with-machine-learning

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## About this course

This workshop focuses on using machine learning to make design decisions. It covers theory and practical applications in various areas, such as facade and urban.

## What you'll learn

- How to recognize optimizable problems in your workflow or tasks and automate them.

- First steps into data science and machine learning

- Analyze existing morphologies/structures and optimize them according to specific criteria.

- Make design decisions based on computed recommendations.

## Methodology

- The workshop will adopt a hands-on approach, combining lectures and practical exercises. Students will work individually on design projects that optimize various design parameters using machine learning and AI methods.





The workshop will cover the following workflows:





- (Design) problem identification and formulation

- Data collection and analysis

- Model selection and utilization.

- Optimization and evaluation

- Design decision-making based on computed recommendations.





The course will primarily focus on the technical aspects of using machine learning for design decision-making. During the course, we will cover both the theory and practical applications of machine learning to optimize parameters in different design scenarios, such as facades, roof structuring, and urban design.

We will explore a range of methods and models, including single-objective optimization and neural network classifiers, to demonstrate how machine learning can be used. While the primary focus will not always be on the aesthetic outcome, we will work on optimizing the most valuable parameters required for the current example.

## Curriculum

### Session 1
- Part 1 (01:00:41)
- Part 2 (57:56)
- Part 3 (02:00:58)

### Session 2
- Part 1 (01:20:59)
- Part 2 (01:24:53)
- Part 3 (01:00:23)