PSY9511 ¨C 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 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 to the course

PhD candidates at the Department of Psychology need to sign up to the course in Studentweb. Please contact the administration if you have problems to sign up in Studentweb.?

PhD candidates at the Department of Psychology will be given priority, but it is also possible for others to apply for the course. Applicants must have at least a Master`s degree. Other candidates can apply to the course through this online form

You will find the registration period in the online form.?

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.

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
• 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.