Machine Learning for Econometrics and Data Science

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Machine Learning for Econometrics and Data Science

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study


  • Language Level

    Taught In English

  • Prerequisites

    Linear Algebra, Probability, Statistics, Econometrics

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    The aim is to explore, study, and develop quantitative learning systems, methods, and algorithms with the purpose to improve the performance with learning from large data sets using powerful computers.

    Machine learning originates from computer science and statistics with the goal of exploring, studying, and developing learning systems,
    methods, and algorithms that can improve their performance with learning from data. This course is designed to provide students an introduction to the main foundations of machine learning. We adopt principles from probability (Bayes rule, conditioning, expectations, independence), linear algebra (vector and matrix operations, eigenvectors, SVD), and calculus (gradients, Jacobians) to propose a formal analysis of the performance of machine learning algorithms. Focusing on the supervised learning framework, we formalise the problem
    of learning to predict based on examples. We introduce the notions of predictor, generalisation risk, Bayes risk and target function,
    empirical error, model and empirical risk minimisation, learning rules, approximation and estimation errors decomposition, and derive learning guarantees under different classification and regression frameworks. We relate these notions to machine learning principles such as model selection, over-fitting, and under-fitting, and techniques such as cross-validation and regularization. In case work we implement learning algorithms and interpret the results.

    Lectures (4 hours, each week) and Tutorials (2 hours, each week)

    Written exam plus an assignment

Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.


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