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Predictive Analysis with Machine Learning 7,5 Credits

Course Contents

This course is an introduction to machine learning and its application to make decisions in business and economics. The machine-learning methods are introduced by starting from a regression perspective and all methods covered are related to the standard regression analysis. The methods covered are regression analysis and classification (starting from the logistic regression-model). We include linear models, non-linear models and tree-based models. We also discuss regularization techniques such as lasso, ridge regression and elastic nets. Further, model selection techniques including information criteria and cross-validation are covered. We also cover bootstrapping methodology, which is a powerful tool for statistical inference.


Connection to Research and Practice
This course covers predictive modelling using machine-learning techniques. This is a fast-growing branch of statistics where analysis of big data is used for predictive and forecasting purposes. Most organizations today use big data for their decision making. The statistical methods introduced in this course enables government organizations, businesses etc. to use the data they collect in a strategic way to improve their operations. Further, they can be used in economic research directly to draw conclusions about unknown economic characteristics in the society.

Prerequisites

The applicants must hold the minimum of a bachelors's degree in Business Aministration or Economics equal to 180 credits including 15 credits in Mathematics/Statistics/Econometrics

Level of Education: Master
Course code/Ladok code: JPAR22
The course is conducted at: Jönköping International Business School

Upcoming course occasions

Type of course
Program
Study type
Campus
Semester
Autumn 2022: Aug 22 - Oct 30
Rate of Study
50%
Language
English
Location
Jönköping
Time
Day
Course coordinator
Kristofer Månsson
Tuition fees do NOT apply for EU/EEA citizens or exchange students
15000kr
Syllabus
HTML  PDF
Application code
HJ-J2122
Last modified 2022-06-02 07:12:32