IN9400 – Machine Learning for Image Analysis
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.
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.
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.
Recommended previous knowledge
- 10 credits overlap with IN5400 – Machine Learning for Image Analysis.
- 10 credits overlap with INF5860 – Machine Learning for Image Analysis (continued).
- 10 credits overlap with INF9860 – Machine Learning for Image Analysis (continued).
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.
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.