Negar Kiyavash

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negar.kiyavash@epfl.ch +41 21 693 02 59 https://sites.google.com/site/negarkiyavash/

EPFL CDM MTEI BAN
ODY 1 01.1 (Odyssea)
Station 5
1015 Lausanne

+41 21 693 02 59
+41 21 693 26 23
Office: ODY 1 01.1
EPFL > CDM > MTEI > BAN

Web site: Web site: https://www.epfl.ch/labs/ban/

+41 21 693 02 59
EPFL > CDM > CDM-MTE > MTE-ENS

+41 21 693 02 59
EPFL > VPA > VPA-FAC > OSSC

+41 21 693 02 59
EPFL > CDM > CDM-DIR > CF-CDM

Web site: Web site: https://www.epfl.ch/schools/cdm/

+41 21 693 02 59
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDMT-GE

Web site: Web site: https://go.epfl.ch/phd-edmt

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Administrative data

Teaching & PhD

Teaching

Management of Technology

PhD Students

Aboueimehrizi Amir Mohammad, Akbari Sina, Elahi Sepehr, Jamshidi Fateme, Khorasani Mohammadsadegh, Kivva Yaroslav, Masiha Mohammadsaeed, Mokhtarian Ehsan, Shahverdikondori Mohammad,

Courses

Causal inference

Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and application will be balanced, with students working directly with network data throughout the course.

Advanced data driven business analytics

This course aims to provide graduate students a grounding in the methods, theory, mathematics and algorithms needed to apply machine learning techniques to in business analytics domain. The course covers topics from machine learning, classical statistics, and data mining.