PSY9511 – Machine learning
Schedule, syllabus and examination date
Course content
The field of machine learning is devoted to building methods that identify patterns in data, in order to make automated predictions or decisions.
Machine learning is becoming an increasingly important method in psychology as datasets increase in size and complexity, e.g., data from registers or brain imaging. The goal of this course is to give PhD students an introduction to machine learning that will enable them to apply these tools in their own research, and to further navigate the literature. The lectures will cover core concepts and give an overview of different methods suitable to different research problems. Through practical exercises, students will get introduced to important software packages like keras and xgboost. The students can choose between using R or Python for the practical exercises.
Learning outcome
Knowledge:
- Understand the concepts supervised learning, unsupervised learning, and reinforcement learning.
- Distinguish between regression and classification, and basic familiarity with core classification methods such as discriminant analysis, na?ve bayes, support vector machines.
- Understand the issue of overfitting, cross-validation, training, test, and validation sets.
- Machine learning methods for variable selection
- Tree based methods (random forests, gradient boosting)
- Deep learning methods for image and text data
- How machine learning differs from classical statistics
- Unsupervised methods such as clustering
Skills:
- Be able to specify machine learning models for new research questions.
- Implement machine learning models using appropriate software.
Admission
This is an elective course in the PhD-programme in psychology. Candidates at UiO can register through studentweb. Candidates from PhD-programmes at other institutions are welcome to apply to the course. Ph.d. candidates enrolled at UiO will be given first priority.
The webform for applicants at other institutions will open in mid May.
Prerequisites
Formal prerequisite knowledge
Enrollment in PhD-programme.
Recommended previous knowledge
Students entering this course should have some background in programming, ideally in R or Python.
Teaching
The teaching will be organized into eight three-hour seminars. The seminars consist of a mix of short lectures introducing they day’s topic, and work on practical exercises supervised by the teacher.
Examination
A total of eight practical exercises need to be handed in.