Deep Learning for Social Scientists
Ph.D. -course
- ECTS credits
- 1
- Course code
- SAMPOL907
- Resources
- Schedule
Course description
Course content
Learning outcomes
Upon successful completion of this course, participants should be able to:
- understand some of the deep learning models that are most commonly used in academic research and industry;
- apply these models to real-world data using Python;
- explain the similarities and differences between these models as well as the advantages and disadvantages they have compared to more classical machine learning methods.
Study period
Credits (ECTS)
Course location
Language of instruction
Course registration and deadlines
Deadline for course registration: April 7, 2024.
Participants apply for admission here
Recommended Previous Knowledge
Participants of this course should have a basic knowledge of linear algebra, calculus, and probability as well as an understanding of "workhorse" statistical models such as linear and logistic regression.
The course will be taught in Python. Prior experience in Python (or another programming language commonly used in machine learning like R) is an advantage but not a requirement.
Compulsory Requirements
Form of assessment
Who may participate
PhD candidates, postdoctoral fellows at the University of Bergen as well as PhD candidates and postdocs from other faculties or institutions.
Exceptions for students enrolled in relevant master's degree programmes and Faculty/staff will be considered if capacity allows, though PhD candidates will be given priority.