ASTK18463U Machine Learning for Social Scientists
Full-degree students enrolled at the Department of Political Science, UCPH
- MSc in Political Science
- MSc in Social Science
- MSc in Security Risk Management
- Bachelor in Political Science
Full-degree students enrolled at the Faculty of Social Science, UCPH
- To be informed
The course is open to:
- Exchange and Guest students from abroad
- Credit students from Danish Universities
- Open University students
The course is scheduled 2 hours weekly. The classroom is booked for 2 hours after lecture, for practicing without lecturer.
This class teaches supervised machine learning methods, i.e. statistical methods for prediction and description tasks. We will focus on methods that are relevant to social science, especially ones using text as data. The programming language used is R.
We will cover (1) text-as-data skills such as preprocessing, TF-IDF, dictionary methods and keyword selection; (2) basics of supervised learning such as performance metrics, train-test splits, cross-validation and regularization; (3) prediction methods such as logistic regression, softmax regression, Naive Bayes models and support vector machines, plus scaling methods like item-response theory models; (4) neural networks and their architecture, including deep neural nets, word embedding models and transformers; and finally (5) applications of neural nets to text-as-data (large language models, text classification).
Knowledge:
Understand the possible models for supervised learning tasks relevant to social sciences, their downsides and upsides depending on the task, and their inner workings.
Skills:
Be able to process data for use by these models, run the models themselves, interpret results, and understand the necessary software and hardware requirements.
Competences:
Be a critical user of supervised machine learning methods for prediction and description problems.
Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. Text as data: A new framework for machine learning and the social sciences. Princeton University Press, 2022.
- Category
- Hours
- Class Instruction
- 28
- Total
- 28
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination
- Type of assessment details
- Mundtlig eksamen MED forberedelse
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
- In the semester where the course takes place: Mundtlig eksamen MED forberedelse
- In subsequent semesters: Free written assignment
Criteria for exam assesment
Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner
Course information
- Language
- English
- Course code
- ASTK18463U
- Credit
- 7,5 ECTS
- Level
- Full Degree MasterBachelor
- Duration
- 1 semester
- Placement
- Spring
Study board
- Department of Political Science, Study Council
Contracting department
- Department of Political Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinators
- Clara Johan E Vandeweerdt (clara.vandeweerdt@ifs.ku.dk)