Syllabus

Title
0364 Advanced Topics in Computing
Instructors
Dipl.-Ing. Rainer Hirk, Ph.D., Florian Schwendinger, Ph.D.
Contact details
rainer.hirk@wu.ac.at; florian.schwendinger@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/03/24 to 09/20/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Elective - Advanced Topics in Statistics and Computing
Dates
Day Date Time Room
Monday 11/25/24 09:00 AM - 12:30 PM D4.0.127
Monday 12/02/24 09:00 AM - 12:30 PM D4.0.127
Monday 12/09/24 09:00 AM - 12:30 PM D4.0.127
Monday 12/16/24 09:00 AM - 12:30 PM D4.0.127
Monday 01/13/25 09:00 AM - 12:30 PM D4.0.127
Monday 01/20/25 09:00 AM - 12:30 PM D4.0.127
Monday 01/27/25 09:00 AM - 12:30 PM D4.0.127

On this page:

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Contents
See the unit description on Canvas WU (lower section).
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Learning outcomes

After completing this course the student will have the ability to:

  • Recall advanced concepts of scientific computing
  • Design and deploy functionality for solving complex computing tasks
  • Use generalized linear models to analyze binary and count data

In addition, the student will be able to:
  • Develop R packages
  • Debug and profile R code
  • Perform High Performance Computing tasks using commodity and special-purpose hardware
  • Use JAGS/BUGS for Bayesian computations

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
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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.

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Teaching/learning method(s)
This course is taught as a lecture combined with homework assignments and course projects.  In combination with the lecture, homework assignments and course projects will help students to consolidate and
expand their knowledge and understanding by developing solutions to theoretical and applied problems, and have to be submitted via email to the lecturer.  Some course projects will be used for structured presentations and discussions.
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Assessment
  • 40% home assignment
  • 40% course projects
  • 20% presentations

The assessment of the homework assignments and course projects 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.
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Prerequisites for participation and waiting lists
Successful completion of at least 42 ECTS credits from the first year compulsory common courses.

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Readings

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Unit details
Unit Date Contents
1

High Performance Computing
2

Generalized Linear Models
3

Bayesian Computing
4

Object-Oriented Programming
5

R Packages
6

R Packages
7

Presentations Exercises HPC
8

Optimization and Root Finding
9

Optimization
10

Presentations and Review
Last edited: 2024-06-24



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