IS 452: Introduction to Machine Learning for Management Decisions

Due to advances in data availability and computing power, machine learning (ML) applications are becoming increasingly important for both industry and public institutions. Examples include HR managers using ML predictions to assess the future job performance of applicants during the hiring process, insurance agents using ML to calculate risk premiums of potential customers, banks using ML to assess the default risk of borrowers, and judges relying on algorithmically generated risk assessments to make bail decisions. Against this backdrop, it is increasingly important to understand how modern ML methods work and how they can support decisions.
This course provides an introduction to the workings, opportunities, and risks of using ML applications to support management decisions. Topics covered include data preparation, integration, analysis, Supervised ML, and the importance of ML applications in decision making processes. Students will implement and apply the methods using the Python programming language and associated libraries.

Learning outcomes
After completing this course, you will

  • be able to understand the role of machine learning for business decisions
  • understand how to transform business problems into machine learning problems
  • have acquired skills to find and analyze patterns in data and to transform the gained knowledge into managerial decisions
  • be able to train and tune machine learning models
  • have gained skills to critically analyze the resulting machine learning models from a business and ethics point of view

Necessary prerequisites
min. 4th semester

Recommended prerequisites
The course serves as an introduction and is therefore accessible to all students (no technical background required). First knowledge in Python is helpful but not necessary.

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS7 SWS
ECTS credits3
Graded yes
Form of assessmentWritten exam (60 min)
Restricted admissionyes
Further informationPortal2
Performing lecturer
Prof. Dr. Kevin Bauer
Prof. Dr. Kevin Bauer
Frequency of offeringSpring semester
Duration of module 1 semester
Range of applicationB.Sc. Bus. Adm., other Bachelor programs (depending on respective study regulations) [9]
Preliminary course work
Program-specific Competency GoalsCG 1, CG 2
LiteratureRaschka, Sebastian (2015): Python Machine Learning, Packt Publishing
Aurélien Géron (2017): Hands-On Machine Learning with Scikit- Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly
Course outline1. Introduction to Data Science & Machine Learning for business
  • Statistics & Machine Learning
  • Data preparation
  • Exploratory data analysis
2. Methods, Algorithms, & Applications
  • Classification & Regression
  • Interpretable Machine Learning