- University: University of Kurdistan Hewlêr
- Department: Computer Engineering Dept.
- My Status: VIsiting Lecturer
- Level: MSc
- Year: 2018-2020
The aim of this module is to give students a firm understanding of the theory underlying the processing and interpretation of visual information and the ability to apply that understanding to ubiquitous computing and entertainment related problems. The module is based around problems so that the technology is always presented in context and during some tutorials students work in groups to design solutions to real world problems using the techniques that they have been taught. In addition, the module has a significant practical component so that students can appreciate how difficult it can be to apply the technology.
On successful completion of the module students should be able to demonstrate:
- Identify and implement appropriate solutions to low, mid and high-level Computer Vision problems.
- Represent problems as mathematical models and apply appropriate machine learning and optimization techniques to solve those problems.
- Apply deep learning algorithms and explain their operation.
- Recommend appropriate statistical representations of static and dynamic objects and apply these to solve detection, classification and/or tracking problems.
- Evaluate the performance of visual classification, tracking and retrieval systems and draw conclusions on their efficacy.
- Introduction to the Course
- Image Classification Pipeline
- Loss Functions and Optimization
- Backpropagation and Neural Networks
- Convolutional Neural Networks
- Training Neural Networks
- Deep Learning Software
- CNN Architectures
- Recurrent Neural Networks
- Detection and Segmentation
- Visualizing and Understanding
- Generative Models