PSY9511 – Machine learning

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 Ph.d. students an introduction to machine learning that will enable them to apply these tools to their own research, and with which 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 gain an introduction 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 of 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 to the course

Master and Ph.d. students can apply for the course.

This is an elective course in the Ph.d.-programme in Psychology.?Ph.d. candidates at The Department of Psychology need to sign up for the course in Studentweb. Please contact the administration if you have problems to sign up in Studentweb.?

Ph.d. candidates at the Department of Psychology will be given priority, but it is also possible for others to apply for the course. You can apply to the course through this online form.

The registration deadline is written in the online form and you will receive an email shortly after the deadline if you are admitted to the course.

All candidates need to be signed up in Studentweb before the first day of teaching.?

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 long seminars. The seminars consist of a mix of short lectures introducing the day’s topic, and work on practical exercises supervised by the teacher.

Examination

A total of eight practical exercises are required for assessment.

Grading scale

Grades are awarded on a pass/fail scale. Read more about?the grading system.

More about examinations at UiO

• Use of sources and citations
• How to use AI as a student
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.