IN9400 – Machine Learning for Image Analysis
Course description
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.
Recommended previous knowledge
Overlapping courses
- 10 credits overlap with IN5400 – Machine Learning for Image Analysis (continued).
- 10 credits overlap with INF5860 – Machine Learning for Image Analysis (continued).
- 10 credits overlap with INF9860 – Machine Learning for Image Analysis (continued).
- 8 credits overlap with IN3310 – Dyp l?ring for bildeanalyse.
- 8 credits overlap with IN4310 – Deep Learning for Image Analysis.
Teaching
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.
Examination
Final home exam which counts 100% towards the final grade
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:?IN4310 – Deep Learning for Image Analysis,?IN3310 – Dyp l?ring for bildeanalyse,?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.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.