PHVIT9530 Måling og målemetoder Emneplan

The course covers the foundations and recent advances in Machine Learning from the point of view of Statistical Learning Theory. The goal of this course is to provide students with the practical skills to support the theoretical knowledge to (1) develop machine learning solutions to challenging problems and (2) to be able to develop the acquired expertise further.

The theoretical aspects of statistical learning will be illustrated with concrete problems and tasks in Python.

No formal requirements over and above the admission requirements.

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is at the forefront of knowledge of conceptual understanding and of theories on the significance of different types of scientific instruments for measuring, testing, evaluating, and assessing health
• is at the forefront of knowledge of national and international mapping systems for health indicators

Skills

The PhD candidate can

• critically evaluate different theories and approaches with methodological significance for measuring, testing, evaluating, and assessing
• apply measurement methods, testing, evaluation, and assessment to their own research project
• critically reflect on and evaluate different psychometric properties of measurement methods, tests, evaluations, and assessments
• address the results of mapping, testing, and evaluation applied in health science research

General competence

The PhD candidate can

• discuss ethical and social issues associated with different types of measurement methods, testing, evaluation, and assessment
• present and discuss knowledge of methods of measurement, testing, and evaluation in the health sciences

This course has three main parts.

The first three weeks cover revisions on statistics and scientific method, briefly presenting also the basics in (Python) programming, selection of a problem, etc. (i.e. revisions of several of the focus points in DATA3800).

After that there will be 7 weeks with presenting and solving exercises, covering the book "Introduction to Statistical Learning".

Finally the course has 3 to 4 weeks bridging the content presented during the weeks before with other scientific fields and topics, namely in Applied AI and in Mathematical Modelling. In particular, connections to the courses " Applied and Computational Mathematics (ACIT4310)" and "Evolutionary Artificial Intelligence and Robotics (ACIT4610)" will be addressed.

The weekly classes will be divided in three parts: (1) a theoretical exposition of the new content introduced each week, (2) one set of exercises/problems implementing the content presented during the theoretical exposition, and (3) supervision of each student in his/her specific project (see "Assignment").

The following required coursework must be approved before the student can take the exam:

A project plan document containing a description of the chosen data set, a preliminary research question and suggested tools and method to apply.

An individual project report approximately 2500 - 5000 words, excluding appendixes.

The exam can be appealed,

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Grade scale A-F.

One internal examiner. External examiners are used periodically.