MSc ACS: Artificial Intelligence / Course details
Year of entry: 2022
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Course unit details:
Cognitive Robotics and Computer Vision
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||Department of Computer Science|
|Available as a free choice unit?||No|
This unit will give students a foundation in the subject of cognitive robotics and machine vision. For the cognitive robotics part, this will involve an introduction to cognitive robotics and the integration of machine learning methods for robots’ cognitive architectures. It will also focus on methods and algorithms for human-robot interaction and social robots and language and speech interfaces to communicate with robots. For the computer vision part, this will involve gaining familiarity with algorithms for low-level and intermediate-level processing and considering the organisation of practical systems. Particular emphasis will be placed on the importance of representation in making explicit prior knowledge, control strategy and interpreting hypotheses. This course unit treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches.
Topics covered in the course include: Introduction to cognitive robotics; Developmental, evolutionary and swarm robotics; Human-robot interaction and social robots; Language and speech interfaces; Deep learning; Introduction to computer vision; Visual object recognition and tracking; Vision-based robot localisation and navigation; 3-D human and hand pose estimation; Motion generation using learnt computational models of human motion.
This course unit is designed for students that are interested in Cognitive Robotics and Human-Robot Interaction, Computer Vision, Artificial Intelligence, or Machine Learning. This course unit is also appropriate for students with an interest in Computer Graphics and/or Robotics.
Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)
- Introduce the basic concepts and algorithmic tools of cognitive robotics and computer vision.
- Introduce the problems of building practical vision systems and cognitive robotic applications.
- Explore the role of representation and inference.
- Explore the statistical processes of image understanding and develop an understanding of advanced concepts and algorithms.
- Discuss novel approaches to designing vision systems and robots that learn
- Develop skills in evaluation of algorithms for the purposes of understanding research publications in this area.
- Have an understanding of common cognitive robotics and machine vision algorithms.
- Have a knowledge of the design of vision algorithms.
- Have a knowledge of the properties of image data and be able to solve problems about extraction of features and other quantitative information.
- Be able to design basic systems for image analysis and cognitive robotics and evaluate and justify the design.
- Be able to write a program for the analysis of image and robotics data and prepare a technical report on the evaluation of this program on suitable test data.
Teaching and learning methods
The unit will consist of interactive lectures and labs (computer vision and machine learning software labs and robot demos).
- Analytical skills
- Group/team working
- Oral communication
- Written communication
|Written assignment (inc essay)||50%|
The assessment for this course unit is based on a combination of coursework and a closed-book exam. The coursework consists of: reports on a set of practical assignments carried out using using robotics and computer vision libraries. Feedback will be provided via Blackboard.
COMP61342 reading list can be found on the Department of Computer Science website for current students.
|Scheduled activity hours|
|Practical classes & workshops||15|
|Independent study hours|
|Angelo Cangelosi||Unit coordinator|
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.