Admission requirements
“Archaeological Science Specialisation Course: Computational Archaeology” obtained.
Students who have not attended the “Archaeological Science Specialisation Course: Computational Archaeology”; please contact the course coordinator to discuss the options.
Description
The course introduces students to some of the core concepts in machine learning, a sub-field of artificial intelligence, at a beginner level. Using archaeological data, students apply their coding skills to understand the computational basics of algorithms. In the course, students will also read and assess published scholarly work relevant to machine learning in archaeology. Through assigned readings, students will discuss their findings in class meetings.
Course objectives
The course has two main aims. The first is to provide working knowledge and computational basics of machine learning. The curriculum covers core concepts such as supervised and unsupervised classification. Students also learn valuable tools and algorithms, such as k-means clustering and random forest and apply these tools to archaeological data. Second, it offers scholarly readings on how machine learning (ML) and artificial intelligence (AI) are utilized in archaeological research. Students completing this course will acquire skills to practice the basics of machine learning and artificial intelligence in archaeology. They will also be able to reflect on those applications.
Timetable
Course schedule details can be found in MyTimetable.
Log in with your ULCN account, and add this course using the 'Add timetable' button.
Mode of instruction
The course has two main modules. In the first module, lecturers present the course material in a class and supervise hands-on coding practices in dedicated lab sessions. In the second module, lecturers guide students in general debates and offer means for reflexive discussions in the class.
Assessment method
Assignment 1 (30%): Lecture Preparation and Presentation
Leveraging your existing knowledge and expertise in quantitative methods, you will choose a specific ML tool or algorithm and design a concise lecture around it. Your lecture should encompass both a theoretical introduction and a practical implementation. This exercise is similar to the first assignment from the previous SCA course. In the MLA course, you will take the lead in organizing and delivering a well-structured, accessible lecture for the audience.
Assignment 2 (40%): Case Analysis
In archaeology, machine learning has been applied to a broad range of research problems, contexts, materials and data types. A grouped list of examples will be provided at the start of the course. Based on your theoretical and practical experience with machine learning acquired so far, you will choose a case study from one of the groups and critically review it regarding data, methods, results, knowledge gained, problems and limitations, and general suitability of the chosen approach. As in assignment 1, the task is to develop and deliver a lecture about the case study that will enable a critical discussion in class of its merits and shortcomings.
Assignment 3 (30%): Final Project
In this assignment, you will build an ML project from scratch. You will be responsible for conceptualizing, conducting, and disseminating your research. Your primary goal will be to document your project journey, with an emphasis on the process itself rather than solely aiming for high classification accuracy.
(You need to submit all of the assignments to pass the course.)
Reading list
Lecturers will periodically assign readings for the in-class discussions. The final reading list will be published before the class based on the number of registered students in the course.
Registration
Enrolment for all components of your study programme through MyStudymap is mandatory. This applies to both compulsory elements and elective credits. If you are not enrolled, you may not participate.
General information about registration can be found on the Course and exam enrolment page.
Contact
For more information about this course, please contact Prof.dr. K. (Karsten) Lambers or Dr. T. (Tuna) Kalaycı.
Remarks
Attendance is compulsory. Students are expected to use their laptops.