About this course

DAT350 gives an introduction to machine learning methods relevant for analysis of healthcare data and fundamentals of medical physics. The following topics are covered:

  • Survival analysis
  • Principles of cancer and radiotherapy
  • Medical imaging (CT, PET, MRI)
  • Segmentation in medical images
  • Biomarker engineering and feature extraction
  • Feature selection for high-dimensional data
  • Multiblock analysis methods for multi-source data
  • Patient outcome prediction models

The course provides an introduction to the basic theoretical properties of the methods, but the main focus is on applied modelling with real datasets. The students will learn to make effective and models that, depending on the application, may support several FN sustainability goals, amongst others 3, 4, 9, 10 and 15.

Learning outcome

Skills and insight into relevant techniques for analysis of healthcare data and principles in medical physics. Basic understanding of various model's mathematical properties and operations. The student will learn to master methods suited for 1) survival analysis, 2) high-dimensional data, 3) medical images, and 4) data from multiple sources. The student will learn to link problems with choice of appropriate analysis methods.

  • Learning activities

    The course will consist of lectures and practical exercises using computers and modern machine learning software (with help from teaching assistants).

  • Teaching support

    Machine data analysis is a subject that constantly evolves, and online learning resources will be connected to lectures and exercises through the course webpages in Canvas.

    The students can also request appointments with the lecturer in his/her office on pre-arranged times and via email.

  • Syllabus

    Curriculum, programs, supporting literature, etc. will be announced on the course web page.

  • Prerequisites

    DAT200 or similar

    DAT300 or similar (can be taken at the same time)

    INF120 or a similar course in basic programming (skills in Python are required)

    INF201/200 or a similar course in advanced programming (version control and good programming practice are assumed to be known)

  • Recommended prerequisites

    FYS241A or similar

  • Assessment method

    Written exam, 3.5 hours. A-F.

    School exam Karakterregel: Letter grades Hjelpemiddel: A1 No calculator, no other aids

  • About use of AI

    Exam: K1 - no use of AI

    Compulsory assignments: K3 - Full use of AI.Use of AI is permitted, but must be in line with the guidelines for the use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme

    An external censor will participate together with the internal censor in forming the exam and censor guide. The external censor checks the internal censor's assessment of a random selection of candidates as a calibration at certain intervals in line with the faculty's guidelines for grading.

  • Mandatory activity

    Compulsory hand-in assignments. Rules for approving obligatory activities will be announced when the course starts.

  • Teaching hours

    Lectures: 4 hours per week. Exercises: 2 hours per week.

  • Admission requirements

    REALFAG