IN5400 – Machine Learning for Image Analysis
Course description
Schedule, syllabus and examination date
Choose semester
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
Recommended previous knowledge
MAT1110 – Calculus and Linear Algebra/MAT1120 – Linear Algebra
Overlapping courses
- 10 credits overlap with IN9400 – 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).
Teaching
2 hours of lectures and 2 hours of exercises each week.
Mandatory assignments must be approved before you can take the exam.
Read more about mandatory assignments and hand-ins here.
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: 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.