0318 Computing
Univ.Prof. Dr. Kurt Hornik
Contact details
Weekly hours
Language of instruction
09/01/23 to 09/22/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Tuesday 10/03/23 08:00 AM - 11:30 AM TC.1.01 OeNB
Thursday 10/12/23 09:00 AM - 12:30 PM TC.2.02
Thursday 10/19/23 09:00 AM - 12:30 PM TC.2.02
Wednesday 10/25/23 09:00 AM - 12:30 PM TC.2.02
Thursday 11/09/23 09:00 AM - 12:30 PM TC.2.02
Thursday 11/16/23 09:00 AM - 12:30 PM TC.2.02
Thursday 11/23/23 11:00 AM - 01:00 PM TC.0.01 ERSTE
Thursday 11/30/23 09:00 AM - 05:00 PM Online-Einheit
See the unit description on Learn@WU (lower section).
Learning outcomes
After completing this course the student will have the ability to:
  • Recall the basic constituents of computer programming (data structures and algorithms)
  • Design, implement, test and debug computer programs for solving mathematical/computational problems
  • Perform matrix computations, solve systems of linear and non-linear equations, and optimize functions of one or several variables.
Apart from that, the course will contribute to the students' ability to:
  • demonstrate effective team skills resulting in an appropriate contribution to the production of a group output
  • work and communicate effectively in a team situation and to function as a valuable and cooperative team member
  • participate in group discussions/team work
Moreover, the student will have the ability to:
  • structure given mathematical/computational tasks and develop algorithms for solving them
  • adequately communicate algorithms and computer programs implementing these
  • "turn ideas into software"
In addition, the student will be able to:
  • Use R for programming and numerical computations
  • Use LaTeX for creating reports and presentations
  • Use BibTeX for managing bibliographic information
Attendance requirements

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

Teaching/learning method(s)

This course is taught as a lecture combined with homework assignments and a course project.

In combination with the lecture, homework assignments will help students to consolidate and expand their knowledge and understanding by developing solutions to theoretical and applied problems, and have to be submitted every week via email to the lecturer. Selected solutions have to be presented in homework colloquia.

For the course project, teams with up to five members will use LaTeX to create a report or presentation of an R-based solution to a given mathematical/computational task.

  • 15% homeworks
  • 25% colloquium
  • 15% final presentations
  • 45% final  

The assessment of the homework assignments and course project will be based on the correctness of results, the clarity and persuasiveness of each bit of work, and the recognizable effort made. This implies an ability to work in teams. For the written exam, the assessment will be based on the ability to describe and apply the key concepts discussed throughout the course and to choose the appropriate analytical techniques to obtain the relevant data.

To avoid the potential free-rider problem related to group work, the final exam will strongly be related to the problems already discussed in homework assignments and course projects. Please note that there will be no opportunity to retake the written final exam.

Prerequisites for participation and waiting lists
  • Basic knowledge in analysis, linear algebra and statistics (on an undergraduate level)
  • Basic computer skills (in particular command of a text editor; on an undergraduate level)

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Availability of lecturer(s)
Unit details
Unit Date Contents

R Basics

After attending this session, students should be able to perform basic computations in R, including constructing and manipulating sequences of numbers and strings, and writing short programs and functions. They will also be able to save and load code and data, and interact with the online R help system.

Reference: Braun and Murdoch, Chapter 2.


R Data Types

After attending this session, students should recall the basic R data types: sequences of logicals, integers, doubles, complex numbers, or strings, and lists; matrix and array structures; and S3 objects such as factors and data frames. They should also recall the principles of numeric floating point computations and be able to use regular expressions for string manipulations.

Reference: Braun and Murdoch, Chapter 2.


R Programming

After attending this session, students should recall the details of flow control elements and functions. They should be able to design, implement, test, debug and profile R programs, and use functional programming concepts including recursion.

Reference: Braun and Murdoch, Chapter 4.


LaTeX, BibTeX and Sweave

After attending this session, students should be able to use LaTeX for creating reports and presentations, and to employ BibTeX for managing bibliographic information. They should also be capable of using Sweave to integrate R code and output into LaTeX documents.

Reference: The Not So Short Introduction to LaTeX 2e.


Numerical Optimization

After attending this session, students should be able to find the zeros of one-dimensional functions, and optimize multivariable functions using Nelder-Mead and Newton-type methods.

Reference: Braun and Murdoch, Chapter 7.


Numerical Linear Algebra

After attending this session, students should be able to perform numerical linear algebra computations in R, including matrix multiplication and inversion, solving systems of linear equations, and obtaining the spectral, singular value, QR and Choleski decompositions.

Reference: Braun and Murdoch, Chapter 6.


Presentations and Review

After attending this session, students should recall developing, presenting and discussing the results of using R for solving mathematical/computational problems. They should also assess their efficiency for self and group organization and reflect upon the "big picture" of this course.

8 Final exam
Last edited: 2023-06-02