IN9400 – Machine Learning for Image Analysis

Schedule, syllabus and examination date

Choose semester

Changes in the course due to coronavirus

Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.

See general guidelines for examination at the MN Faculty autumn 2020.

Course content

The course gives an introduction to the theory behind central machine learning algorithms and how these are used in image analysis. Selected methods and tools for deep learning are also presented.

Learning outcome

After finishing the course you´ll:

  • have good knowledge of how neural networks are built up and how backpropagation works
  • have a good knowledge of how a web is practiced in practice and how the training process can be monitored
  • know the key mathematical methods used in the algorithms
  • know different network architectures and in what contexts they are suitable
  • have knowledge of overtime, generalization, and validation and how best possible generalization can be achieved
  • know how the convolutions network works and how these can be customized for different purposes.
  • have basic knowledge in topics such as unsupervised learning, recurrent networks, and reinforcement learning.
  • have experience in using deep learning tools such as Tensorflow

The PhD-variant will also look at selected new research articles within deep learning.

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.


Overlapping courses


2 hours lectures and 2 hours exercises every week.

Mandatory assignments must be approved before you can take the exam. Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments. Compulsory attendance at first lecture.


4 hours written digital examination or an oral examination, depending on the number of students.

All mandatory assignments must be approved before you can take the exam.

It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: INF9860 - Machine Learning for Image Analysis (continued)INF5860 - Machine Learning for Image Analysis (continued)IN5400 - Machine Learning for Image Analysis

Examination support material

No examination support material is allowed.

Grading scale

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

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Oct. 28, 2020 4:15:09 PM

Facts about this course

Teaching language
Norwegian (English on request)