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Smart Cities - Management of Smart Urban Infrastructures, Online Course, EPFL University, 2022
Machine Learning Foundations: A Case Study Approach, University of Washington, 2021
Certificate of Build Digi Craft, New mindset for high-quality European Baukultur, ISP2, 2021
Renewable Energy and Green Building Entrepreneurship, Online course, Duke University, 2020
Workshop of Architectural Sketch, Shaqaghi, IAcenter , 2016
The complete guide to Autodesk Revit architecture, Online course, Udemy, 2020
Certificate of learning Rhino Architecture, IAcenter, 2015
Certificate of Advanced 3Dmax and V-ray, International Tehran Institute of Technology, 2013
University of Washington, 2021
Emily Fox, Amazon Professor of Machine Learning
Carlos Guestrin, Amazon Professor of ML
-Identify potential applications of machine learning in practice.
-Describe the core differences in analyses enabled by regression, classification, and clustering.
-Select the appropriate machine learning task for a potential application.
-Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
-Represent your data as features to serve as input to machine learning models.
-Assess the model quality in terms of relevant error metrics for each task.
-Utilize a dataset to fit a model to analyze new data.
-Build an end-to-end application that uses machine learning at its core.
-Implement these techniques in Python.
Certificate of Build Digi Craft, New mindset for high-quality European Baukultur, ISP2, 2021
BuildDigiCraft explores the interrelation of the new digital tools, the traditional building craft techniques, the current design practice and the values behind the process of shaping the built environment.
How do we shape the future built environment in a world of growing digitalization and professional specialization?
OVERVIEW of the required preparatory tasks for ISP2: Digital Futures The participants are asked to prepare certain documents for the workshop days, defined by five preparatory tasks:
● Day 1: Personal Presentation by reflecting your individual project - due February 14th
● Day 2: Digital Process Modelling - due February 14th
● Day 3: Material: non-living vs. Living -to be defined by February 10th- - due February 14th
● Day 4: Knowledge Transfer -to be defined by February 10th- - due February 14th
● Day 5: Individual SWOT-Analysis - to be done no earlier than February 19th
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