IN5400 – Machine Learning for Image Analysis

Schedule, syllabus and examination date

Choose semester

Changes in the course due to coronavirus

Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.

Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.

Course content

The course provide an introduction to the theory behind key machine learning algorithms used in image analysis. Furthermore, selected methods and tools for deep learning are described.

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

Admission to the course

Students admitted at UiO must apply for courses in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

Nordic citizens and applicants residing in the Nordic countries may apply to take this course as a single course student.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants

MAT1110 – Calculus and Linear Algebra/MAT1120 – Linear Algebra

Overlapping courses

Teaching

2 hours of lectures and 2 hours of exercises each week.

Mandatory assignments must be approved before you can take the exam.

Examination

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 scale from A to F, where A is the best grade and F is a fail. 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) Aug. 3, 2020 5:16:08 PM

Facts about this course

Credits
10
Level
Master
Teaching
Spring
Examination
Spring
Teaching language
Norwegian (English on request)