Machine Learning together with data streams offer a new and universal way of looking at the world phenomena, which is radically different than classical disciplinary and theory based approaches. Opposite to theory driven approaches, machine learning, which is a relatively new field of research inverts the process of scientific modeling. By asking a proper question and having a lot of observations around that question, machine learning promises to learn good answers from the provided data sets. Therefore, the trained models have a larger capacity to deal with unique and complex problems that have no a priori descriptive theories. With this conceptual turn, within the last few years we have experienced an incredible progress in very complex application domains such as natural language modeling, which led to automatic language translations between many spoken languages without a need to directly encode the semantic or syntactical rules of those languages. Also, there are several success stories in computer vision and specially the new field of deep learning, which have led to successful projects such as self-driving cars.
However, while new machine learning algorithms such as deep learning have created a new wave of applications in computer vision and natural language translation, there are a lot of complex applications, yet to be investigated.
Similar to the way engineering students need to learn calculus and differential equations or similar to the way architects learn about drawing with pencil and paper, now students and researchers need to get literate in these new fields. In this technical seminar series, our objectives are two folds. While we discuss many mathematical and computational techniques for data driven modeling, we present a wide variety of different applications from different domains. It is expected that graduate researchers will learn to approach their own problems with these new style of thinking.
Dates: Tuesdays 14:00-16:00
Introduction: Tuesday, October 3, 2017
Place: Chair for CAAD, D-ARCH/ITA/CAAD HIB E16
Course tutor: Vahid Moosavi
The materials in this semester are mainly from the last year with some modifications. This repository will be updated on a weekly basis. However, Github repository of data driven modeling seminars in 2016 is also available from here.