Machine Learning for Econometrics and Data Science

2019-2020

Course Objective

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.

Course Content

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 principles, algorithms, and applications of machine
learning. It includes topics related to supervised learning algorithms
for classification problems (logistic regression, support vector
machine), for regression problems (ridge regression, LASSO), but also
unsupervised learning algorithms (k-means, clustering, linear and
nonlinear dimensionality reduction). We adopt principles from
probability (Bayes rule, conditioning, expectations, independence),
linear algebra (vector and matrix operations, eigenvectors, SVD), and
calculus (gradients, Jacobians) to derive machine learning methods. We
further discuss 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 appropriate supervised and
unsupervised learning algorithms on real and synthetic data sets and
interpret the results.

Teaching Methods

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

Method of Assessment

Written Exam (individual) plus an Assignment (in groups)

Recommended background knowledge

Basic level of Linear Algebra, Statistics and Econometrics

General Information

Course Code E_EOR3_MLEDS
Credits 6 EC
Period P4
Course Level 300
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. ing. S.F. Fries
Examiner dr. ing. S.F. Fries
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Study Group, Lecture, Computer lab
Target audiences

This course is also available as: