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PTMBA2022 Workshop Machine Learning

Course code
WS013
Course type
PT MBA Lecture
Weekly Hours
2,0
ECTS
0
Term
FS 2021
Language
Englisch
Lecturers
Juniorprof. Dr. Martin Prause
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.

The digital society is characterized by producing and interrelating a large amount of data from all kinds of sources. To turn (big) data into meaning full information that can feed business models and create competitive advantages, managers should have a sound understanding of the potential and limits of information extraction and processing techniques such as Data Mining and in particular Machine Learning.

Data Mining is the extraction of implicit, previously unknown and potentially useful information from data. Machine learning is an automated process that extracts patterns from data to build models used in predictive data analytics. Machine Learning algorithms automate the process of learning a particular model.

The objective of this course is to provide the technical background for data handling, data cleaning and preparation (structured/unstructured, real-time, sparse/incomplete data) and Machine Learning algorithms (supervised learning, unsupervised learning) to assess their managerial applicability.

We will use the language R for implementing Machine Learning algorithms on classification, clustering, and associations tasks for predictive analysis in the fields of marketing, finance, supply chain management, and economics. The theoretical content is complemented by hands-on activities for processing and analyzing real-time data from social networks and other databases. We’ll get our hands dirty in programming and we will look behind the scene of Machine Learning concepts and Artificial Intelligence to assess their business (added) value properly.

Date Time
Friday, 19.03.2021 09:45 - 17:00
Saturday, 20.03.2021 09:45 - 17:00
Sunday, 21.03.2021 09:45 - 17:00
Learning Goals
  • Machine Learning and Artificial Intelligence in the business context
  • Introduction to the programming language R
  • Data exploration and preparation
  • Classification with nearest-neighbour and decision trees applied to economics and finance
  • Clustering with k-means applied to marketing
  • Association Analysis applied to marketing and supply chain
  • Sentiment Analysis of live twitter data applied to social marketing
  • Pattern Recognition with Neural Network and its derivatives
  • Use cases of deep learning and current developments
Literature:Grolemund, Hands-on Programming with R, https://rstudio-education.github.io/hopr/Wickham & Grolemund, R for Data Science, https://r4ds.had.co.nz/Kelleher, Namee, and D’arcy (2015), Fundamentals of Machine Learning for Predictive Data Analytics, MIT PressEllis (2014), Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, Wiley & Sons.Hand, Mannila, and Smyth (2001), Principles of Data Mining, MIT Press
Lecture, Coding exercises, Follow-me-through-the-code, Guest lecture
None, participation is sufficient
None. The required programming skills to code Machine Learning algorithms and handle large amount of data will be taught in prep-material and in class.
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