Advanced Mathematics

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Advanced Mathematics

This background course on mathematics aims to provide fundamental mathematical knowledge essential for advanced economic analysis. Although open to all master students, it is specifically tailored to those wishing to directly pursue the advanced Y-track of courses. Therefore in content and form, this intensive course is intended to deliver methods beyond refreshing advanced calculus and linear algebra. The course solely deals with deterministic mathematics. For some theorems formally rigorous proofs are presented in order to make participants more comfortable with - and ideally to provide some intuition for – constructing and understanding of mathematical proofs. Throughout the course proper use of notation will be stressed. Topics presented in class constitute the minimal required program given the above aim, and the maximal feasible program given time.

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  • 46 Students Enrolled
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Tags:
Mathematics LinearAlgebra Vectorspaces



Courselet Content

4 courselets • 19 courselet components • 15h 53m total length
Sets, Relations, Preferences - Part 1
33min
Sets, Relations, Preferences - Part 2
39min
69min
Sets, Relations, Preferences - Part 4
57min
Sets, Relations, Preferences - Part 5 - Complex Numbers
73min
mb

Requirements

  • Knowledge of Calculus I & II

Description

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About the Instructor

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About the Instructor

Kainat Khowaja is currently a research associate and PhD student at Humboldt University of Berlin with International Research Training Group IRTG1792 “High dimensional non stationary time series analysis”.

Along with a double masters degree in Mathematics and high international exposure through various exchanges. she has a rich background in research, scientific presentations and teaching. Since 2015, she has worked as a teaching assistant for 4+ courses and completed various research projects related to statistics, econometrics, machine learning and finance. She has also co-taught four graduate level courses related to smart data analysis and mathematical statistics at her current institute.

Presently, she is working on the methodology to construct uniform confidence bands around non-parametric estimates from generalized random forests with assistance of multiplier bootstrap. Her work extends on the scope in which mathematical properties can be utilized to bridge the gap between theoretical understanding of random forests and their practical performance. Her research interests include interpretability of and variable selection through random forests and she would like to continue her research in the same area.

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