Business Analytics and Machine Learning

What it is about

You learn methods of statistical analysis and machine learning for classification, numerical prediction and clustering. After the module you can explain the assumptions and workings of common procedures and analyze datasets with R as well as interpret results.

What you will be able to do

Familiarize with common procedures for classification, numerical prediction and clustering
Name the assumptions of various procedures
Understand how selected methods work
Classify applications in business and economic questions
Analyze datasets with R and interpret results

What the module consists of

VorlesungThe instructor conveys the content and suitable literature; introduction to methods and their applications
ÜbungSupervised individual and group work on exercises and datasets; practical application and programming exercises in R or Python

Teaching method

VorlesungConveying the theoretical foundations and methodological knowledge
Übung / betreute ArbeitApplication of procedures to exercises and real datasets; development of one's own data-based solutions
GruppenarbeitWorking on problems with datasets to foster collaborative problem solving
Programmierpraxis (R/Python)Practice of technical skills for implementation and analysis

What you should already know

MA0901 Lineare Algebra für Informatikrecommended
MA0902 Analysis für Informatikrecommended
IN0018 Diskrete Wahrscheinlichkeitstheorierecommended
MA9712 Statistikrecommended

Assessment

Written examWritten testProject workReportPresentationOral examPortfolioListening comprehension

A 90-minute exam. By solving problems you demonstrate that you understand the workings and assumptions of different methods, can interpret results and assess model quality, and can independently construct analytical solution paths. (No information on aids or weighting in the raw data record.)

Reading list

Trevor Hastie, Jerome Friedman, Robert Tibshirani - Elements of Statistical Learning
Ian Witten, Eibe Frank, Mark Hall, Christopher Pal - Data Mining: Practical Machine Learning Tools and Techniques
James H. Stock, Mark W. Watson - Introduction to Econometrics
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - An Introduction to Statistical Learning

Dates

LectureBusiness Analytics and Machine Learning (IN2028)

Mon14:00–16:00
003, Hörsaal 2, "Interims II" (5416.01.003)
14× · 19.10.–01.02.
- 19.10.
- 26.10.
- 02.11.
- 09.11.
- 16.11.
- 23.11.
- 30.11.
- 07.12.
- 14.12.
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- 11.01.
- 18.01.
- 25.01.
- 01.02.

ExerciseÜbungen zu Business Analytics and Machine Learning (IN2028)5 groups to choose from

ATue10:00–12:00
Online: Videokonferenz
14× · 13.10.–02.02.
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- 20.10.
- 27.10.
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- 24.11.
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- 02.02.
BTue14:00–16:00
Online: Videokonferenz
15× · 13.10.–02.02.
- 13.10.
- 20.10.
- 27.10.
- 03.11.
- 10.11.
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- 24.11.
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- 02.02.
CThu10:00–12:00
01.10.011, Seminarraum (Inf. 18/19 (5610.01.011)
14× · 15.10.–04.02.
- 15.10.
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DWed10:00–12:00
01.10.011, Seminarraum (Inf. 18/19 (5610.01.011)
14× · 14.10.–03.02.
- 14.10.
- 21.10.
- 04.11.
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EWed12:00–14:00
01.10.011, Seminarraum (Inf. 18/19 (5610.01.011)
15× · 14.10.–03.02.
- 14.10.
- 21.10.
- 28.10.
- 04.11.
- 11.11.
- 18.11.
- 25.11.
- 02.12.
- 09.12.
- 16.12.
- 23.12.
- 13.01.
- 20.01.
- 27.01.
- 03.02.

From the current semester, not binding. You attend one of several groups; the timetable automatically suggests the one with the fewest clashes.

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