What you'll learn

Participants in this course will encounter a basic introduction to Information Technologies.

You will learn about artificial intelligence and its subtopics and understand machine learning and deep learning without learning coding.

You will learn artificial neural networks and coding with simple applications and will have a clear and general knowledge of AI and IT.

You will learn by examining the studies, publications and methods of a doctoral thesis in the field of Landscape Architecture and Artificial Intelligence.

Requirements

There is no experience requirement.

Description

An introductory course on IT and Artificial Intelligence (AI) was presented, covering the topics listed below. This course aims to teach AI and its related concepts to students with no prior knowledge of IT or AI, without requiring any coding skills. It is aimed to clearly convey the experience gained through academic publications and all the details of the doctoral thesis that try to bring together the subjects of landscape architecture and artificial intelligence. It is planned to be a short introduction to artificial intelligence for architecture, urban planning and landscape architecture students who want to develop new methods and approaches. This content was created with the feedback received from educational institutions and professional vocational institutions that requested this simple introduction training. A curriculum that explains the subject of AI, which is considered complex and difficult to learn, in a simple and organized way was created. In addition to explaining it to disciplines interested in built environment design, it was also intended to be explained in a structure that artists and other enthusiasts can understand. It is an introduction to the field of IT, where the knowledge and experience learned from dozens of online courses of Udemy and universities during the six-year intensive education process is briefly transferred.Topics covered:Historical DevelopmentBasic ConceptsGenerative Models and Built Environment RelationshipMachine LearningArtificial Neural NetworksGenerative ModelsCAD, BIM, GIS, and AILarge Language ModelsPrompt EngineeringAutodesk Forma ApplicationEthicsConclusionsAdditionals