PHBA8100 Research ethics and professional standards 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 acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

Students will be able to:

• describe and discuss the values and rules of behavior analysis through primary and secondary source writings on ethics and professional issues (e.g., submitting or reviewing original research)
• describe and discuss professional codes of conduct relevant to specific fields of work in behavior analysis
• describe and discuss the application of these value systems and professional codes to their own clinical, educational, and research activities
• follow the guidelines set down in the APA Publication Manual when publishing scientific work and when participating in public discussions
• exhibit honesty, objectivity, integrity, carefulness, openness, respect for intellectual property, confidentiality, responsible publication, responsible mentoring, respect for colleagues, social responsibility, non-discrimination, competence, legality, animal care (if relevant), and human subjects protection
• describe and discuss institutional review board processes and human subjects research guidelines
• describe and discuss the logic and ethical application of single-subject and traditional group designs

This course is divided into two parts. The first part with focus on covering the principles of Statistical Learning. Different seminars will be given on the different methodological aspects of Statistical learning, mainly, supervised learning and unsupervised learning.

The second part will focus on the students completing a programming project. This is a real data analysis problem, where the student is asked to carry out the analysis using the tools and techniques from the course and hand in a report documenting the steps taken in the analysis. The ultimate goal is to build a predictive model.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

The course will also include practical training / lab sessions.

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.

Grade scale A-F.

No overlap with existing courses.