Instructor and Course Developer

Applied Machine Learning in Economics

  • This introductory course gives an overview of different concepts, techniques, and algorithms in machine learning and their applications in economics. We begin with topics such as classification, linear and non-linear regressions and end with more recent topics such as bagging, Randome Forests, boosting, support vector machines, and Neural Networks. This course will give students the basic knowledge behind these machine learning methods and the ability to utilize them in an economic setting. Students will be led and mentored to develop and solve an economic problem with machine learning methods introduced during the course.
  • You can find the Fall 2019 Syllabus and Slides in the course website.
  • Outstanding rating (top 5% intructors) in the “List of Teachers Ranked as Excellent by their Students

    Teacher Assistant

    University of Illinois (Undergraduate-Level)

  • Intoduction to Microeconomics
    • Awarded in the “List of Teachers Ranked as Excellent by their Students” for 5 consecutive semesters
  • Intermediate Microeconomics

Sharif University of Technology (Graduate-Level)

  • Econometrics I
  • Game Theory
  • Microeconomics II