IN4310 – Deep Learning for Image Analysis
Course description
Course content
This course teaches common methods in deep learning applied to image data, covering key deep learning algorithms and concepts for training neural networks. The course's focus is on supervised learning and image classification. Nevertheless, the course will also introduce other common learning regimes and image analysis tasks, such as image segmentation and object detection.
Learning outcome
After this course you will:?
- understand how neural networks are built and how backpropagation works;?
- understand empirical risk minimization and be familiar with popular loss and activation functions?
- understand key mathematical insights and intuition behind the training process, how to handle distribution shifts and generalize, and regularization techniques;?
- know how to train a neural network from scratch and basics of popular first-order optimization methods, use pre-trained models, fine tune the neural networks, and discern when to use each approach to solve a problem;?
- understand how to treat the data (augment and clean it) to improve the efficacy of neural networks;?
- know different network architectures and in what contexts they are suitable;?
- understand the inductive bias of locality imposed by convolutions, its implementation on convolutional neural networks, and its application to imaging;?
- go beyond supervised learning and understand the basics of self-supervised learning;?
- know how to apply deep learning to solve problems that depend on imaging data, for example, image classification, object segmentation, object detection, among others; ?
- have experience in using Pytorch.?
Admission to the course
Students admitted at UiO must?apply for courses?in Studentweb. Students enrolled in Master's Degree Programmes belonging to other Departments than IFI can, on application, be admitted to the course if this is cleared by their own study programme.
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
The student ought to have a strong background in?
- programming, for this we recommend IN2010;?
- mathematics (calculus and linear algebra), we recommend that the student has taken MAT1110, and it will be good to have MAT1120;?
- statistics and machine learning, we recommend that the student has taken either FYS-STK3155 or STK-IN4300.?
It is also recommended to have some knowledge on applications related to images, such as from IN2070.?
Overlapping courses
- 10 credits overlap with IN3310 – Dyp l?ring for bildeanalyse.
- 8 credits overlap with IN5400 – Machine Learning for Image Analysis (continued).
- 8 credits overlap with IN9400 – Machine Learning for Image Analysis (discontinued).
- 8 credits overlap with INF5860 – Machine Learning for Image Analysis (continued).
- 8 credits overlap with INF9860 – Machine Learning for Image Analysis (continued).
Teaching
2 hours of lectures and 2 hours of exercises each week.
The course has mandatory assignments that must be approved before the exam.? Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments .
Examination
Written exam (4 hours).?
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: IN3310 – Dyp l?ring for bildeanalyse, IN5400 – Machine Learning for Image Analysis (continued), IN9400 – Machine Learning for Image Analysis (discontinued), INF5860, INF9860
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.?
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.