Deep Learning for Image Analysis
Course, Master's level, 1MD120
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 March 2024–2 June 2024
- Language of instruction
- English
- Entry requirements
-
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.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- Application fee: SEK 900
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 16 October 2023
- Application code
- UU-61612
Admitted or on the waiting list?
- Registration period
- 4 March 2024–25 March 2024
- Information on registration
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 March 2024–2 June 2024
- Language of instruction
- English
- Entry requirements
-
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.
Admitted or on the waiting list?
- Registration period
- 4 March 2024–25 March 2024
- Information on registration
About the course
The course covers deep learning for visual data such as data-driven image classification, linear classification and backpropagation. It covers convolutional neural networks (CNN) and methods for training, visualising and interpreting these, generative adversarial networks (GANs), different architectures and applications within image analysis (classification, detection, segmentation). The possibilities and limitations of deep learning, in particular in image analysis, are discussed.
Reading list
No reading list found.