This is a short introduction of my lecture series on Data Driven Modeling, this semester under the theme of “Coding As Literacy”…
CODING AS LITERACY
How to look at our world with Big Data and Machine Learning
Machine Learning and Big Data together offer a universal way of looking at the world phenomena, which is radically different than the classical expert based disciplinary view. However, in order to fully grasp this new potentiality, we need a new set of viewpoints, skills and technologies that we call “Coding As Literacy”.
We believe that this literacy will turn the classical notion of expertise as “Having the Answers to the Known Questions” to “Learning to Ask Good Questions”, where the answers can be found with an appropriate level of coding literacy.
As the continuation of the last semester, we will discuss Machine Learning and Data Driven Techniques, in two different scenarios. While in some of the sessions we focus on Machine Learning Algorithms in a rather theoretical level, in some sessions we would like to have tutorials and showcases on data collection through APIs or web crawling, data processing and full stack development of data driven applications in Urban Design, Architectural Design, Natural Language Processing, Social Media Analysis, Real Estate Market, Food Industry and any other applied problem that you suggest! We would like to see the possibilities of these new skills in any application domain!
Some of the technical keywords: Machine Learning in Python, Scikit-learn, Gensim, Tensorflow, Deep Learning, and Ensemble Models
By attending to this seminars you can register for the following course:
PhD Colloquium: Theory of Information Technology for Architects: https://goo.gl/wLYjf3
Dates: Tuesdays 14:00 – 16:00
Starting: Tuesday, February 28, 2017
Place: Chair for CAAD, D-ARCH / ITA / CAAD HIB E 16
Course tutor: Vahid Moosavi
GitHub Repository: https://github.com/sevamoo/data_driven_modeling