IN3310 – Deep Learning for Image Analysis
Course description
Schedule, syllabus and examination date
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 key mathematical insights and intuition behind the training process, how to check and avoid underfit and overfit during training to facilitate generalization, and regularization techniques for a neural network;?
know how to train a neural network from scratch, use pre-trained models, finetune 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;?
understand the supervised learning regime;?
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 who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for?in Studentweb.
Special admission requirements
In addition to fulfilling the?Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
Mathematics R1 or Mathematics (S1+S2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies. Read more about?special admission requirements?(in Norwegian).
Formal prerequisite knowledge
FYS-STK3155 – Applied Data Analysis and Machine Learning or STK2100 – Machine Learning and Statistical Methods for Prediction and Classification?
Recommended previous knowledge
The student should 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;?
Its also recommended to have some knowledge on applications related to images, such as from IN2070.?
Overlapping courses
- 10 credits overlap with IN4310 – Deep Learning for Image Analysis.
- 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.
This 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: IN4310 – Deep Learning for Image Analysis, 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.