Machine Learning and Decision Making

General Remarks

First lecture on Thursday, 24.05. 2018. Further information and downloads available via Moodle.

Characterizing the course

It is important to differentiate between anecdotal evidence and evidence that can be tested and proved. Understanding and analyzing data is essential for the latter. All of us in day-to-day routine use numbers in our calculations. Problems in business contain a great degree of quantitative element in the form of facts and figures. It is essential for professionals to carry out data analysis and interpretation for effective decisions. In this context, they need to prepare quantitative arguments to justify their decisions. Decision making using machine learning techniques is the answer for accomplishing this purpose. The course aims to develop your analytical skills - both the ability to conceptualize a problem as well as solve them broadly, it aims to make you understand and appreciate the most widely used tools of machine learning  which form the basis for rational and sound  decisions

  • Focus on problem recognition and test hypothesis/model in the context of managerial decision-making.
  • Develop skills in analysis and interpretation of data
  • Handle challenging problems using appropriate analysis tools

Course Content

-   Introduction to analytics using machine learning

-   Introduction to R Studio and QGIS

-   Unsupervised learning

-   Supervised learning: Continuous response and classification

-   Boosting and bagging

-   Model comparisons

Literature

  • Gareth, J., D. Witten, T. Hastie and R. Tibshirani, “An Introduction to Statistical Learning with Applications in R”, Springer series.
  • Additional references will be provided in class.

News

This course is a 4 credit point course that will be delivered during an eight-week period from mid-May to mid July. You will find further information on Moodle in due course.

Dates and Room

Sessions will be held in the period from mid-May to mid-July.

Sessions will be on Thursdays from 16-18 (HeHo 18, E20) and on Fridays from 14-16 (HeHo 18, 120).

First lecture on Thursday, 24.05.2018.

A detailed schedule will be made available on Moodle.

Exam

Grading is based on a paper that you submit according to instructions given at the start of the course. There will be no written exam.

 

 

Module description

This lecture earns you 4 credit points and it is open for

  • Wiwi (BSc, MSc, Dipl)
  • WiMa/WiPhy (BSc, MSc, Dipl)
  • Finance (MSc)

and others according to study plans.