Deep Learning for Image Analysis
Syllabus, Master's level, 1MD120
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computer Science A1F, Image Analysis and Machine Learning A1F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 8 October 2020
- Responsible department
- Department of Information Technology
120 credits including Statistical Machine Learning, calculus of several variables, a second programming course and Introduction to Image Analysis or Computer-Assisted Image Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
After passing the course the student should be able to
- describe and use backpropagation together with gradient descent and stochastic gradient descent to optimize a model;
- implement, from the ground up, a fully interconnected multilayer neural network;
- explain under- and over-fitting and what can be done to avoid these;
- describe and use different kinds of regularization techniques;
- use modern environments for deep machine learning to solve practical image processing and image analysis problems;
- describe and use deep convolutional networks for image classification, object detection and image segmentation;
- critically analyse research in the main field, assess its possibilities and limitations.
Deep learning for visual data. Data-driven image classification, linear classification, activation functions, various cost functions, gradient-based optimization with backpropagation. Convolutional neural networks (CNN) and methods for training them, transfer learning and data augmentation. Different architectures and applications in image analysis (classification, detection, segmentation). Visualization and understanding of convolutional neural networks. Generative Adversarial Networks (GANs). Possibilities and limitations with deep learning.
Lectures, assignments, computer exercises and project work in groups.
Written exam (3 credits), approved assignments and laboratory work, oral and written presentation of project work (4.5 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
No reading list found.