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MSc ACS: Artificial Intelligence / Course details

Year of entry: 2022

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Course unit details:
Cognitive Robotics and Computer Vision

Course unit fact file
Unit code COMP61342
Credit rating 15
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

Overview

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.

Pre/co-requisites

Pre-requisites

Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)

Aims

  • 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.
     

Learning outcomes

  • 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).

Employability skills

Analytical skills
Group/team working
Oral communication
Research
Written communication

Assessment methods

Method Weight
Written exam 50%
Written assignment (inc essay) 50%

Feedback methods

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.

Recommended reading

COMP61342 reading list can be found on the Department of Computer Science website for current students. 

Study hours

Scheduled activity hours
Lectures 15
Practical classes & workshops 15
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Angelo Cangelosi Unit coordinator

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.

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