I857 – Computational Archaeology

Module
Computational Archaeology
Computational Archaeology

Please note: This module will be offered for the first time in the Summer semester 2025 semester.
Module number
I857
Version: 1
Faculty
Informatics/Mathematics
Level
Master
Duration
1 Semester
Semester
Summer semester
Module supervisor

PD Prof. Dr. rer. nat. Marco Block-Berlitz
marco.block-berlitz(at)htw-dresden.de
Lecturer(s)

PD Prof. Dr. rer. nat. Marco Block-Berlitz
marco.block-berlitz(at)htw-dresden.de
Course language(s)

English
in "Computational Archaeology"
ECTS credits

5.00 credits

Workload

150 hours

Courses

4.00 SCH (2.00 SCH Lecture | 2.00 SCH Internship)
Self-study time

90.00 hours

Pre-examination(s)
None
Examination(s)

Written examination
Module examination | Examination time: 90 min | Weighting: 100% | tested in English language
in "Computational Archaeology"
Form of teaching
keine Angabe
Media type

  • Lecture materials are available as videos and the slides as PDFs
  • In addition to the lecture questionnaire, which is available at the end of each lecture, voluntary practical and theoretical exercises are offered
Instruction content/structure
  • Image processing
  • Pattern recognition
  • Agent-based modelling
  • Procedural generation
Qualification objectives

"They" will be used to shorten students in the remainder of this document to keep the objectives more compact.

  • They are able to master the necessary mathematical basics
  • They will be able to decide in a goal-oriented manner which methods of image processing will improve or prepare the data for a given problem.
  • They are able to apply simple methods of image processing.
  • They are able to apply simple pattern recognition methods to a given database.
  • They are qualified to understand agent-based models and are able to design simple simulations on their own
  • They are familiar with simple concepts of procedural generation of data
Social and personal skills
No information
Special admission requirements
Recommended prerequisites

It is recomended to complete modul I860 Applied Mathematics and Computer Science before taking this modul.
Continuation options
Literature
  • Lambert: A Student's Guide to Bayesian Statistics, Sage Publishing, 2018
  • Zaki, Meira: Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Cambridge University Press, sec. ed., 2020
  • Romanowska, Wren, Crabtree: Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies, ‎ Santa Fe Institute Press, 2021
  • Shiffman: The Nature of Code: Simulating Natural Systems with Processing, The Nature of Code, 2012
Current teaching resources

Script to the course und lectures notes (videos und slides as PDF)
Notes
No information
Link to course/learning resources in OPAL
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