Applications of Computer Science

University of Reading

Course Description

  • Course Name

    Applications of Computer Science

  • Host University

    University of Reading

  • Location

    Reading, England

  • Area of Study

    Computer Science

  • Language Level

    Taught In English

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    Module Provider: School of Mathematical and Physical Sciences
    Number of credits: 20 [10 ECTS credits]
    Terms in which taught: Autumn / Spring / Summer module
    Non-modular pre-requisites:
    Modules excluded:
    Module version for: 2016/7

    Summary module description:
    This module introduces popular applications associated with computers, including artificial intelligence, robotics, computer vision and graphics, and data analytics.

    The module aims to broaden students? knowledge of computer science with its applications in the key areas to enhance their understanding to the discipline.

    Assessable learning outcomes:
    Students completing this module should be able to describe typical techniques and apply relevant algorithms to artificial intelligence and robots, to use basic algorithms describing tasks involved in computer vision and computer graphics; and to deal with data workflows with relevant data analytical tools.

    Additional outcomes:
    Outline content:
    The module consists of four application components, as listed below

    -Artificial intelligence: here various methods are discussed which are used for ?intelligent? computing machines; it is also shown how some of these methods, such as neural networks and evolutionary computing, have been inspired by natural systems. Applications for artificial intelligence algorithms are also considered.

    -Robotics: a cybernetic approach to the subject is taken, showing how the theme of feedback is key to the control of, interaction with and learning by robots. Different types and applications of robots are described, their ?brain?, sensors and actuators are explained, interaction between robots and between humans and robots are considered, and links with artificial life and artificial intelligence are explored.

    -Computer vision and graphics: typical cases of successful computer vision applications are introduced. Techniques of computer graphics supporting games and virtual reality are presented.

    -Data analytics: concepts, techniques and tools for the extraction of information from data and for the design and execution of data workflows are introduced.
    Brief description of teaching and learning methods:
    The module comprises 2 lectures per week, associated laboratory practicals, assignments and some revision tutorials. Laboratory practicals are used to reinforce the relevant lectures.

    Contact hours:
    Lectures- 20
    Practicals classes and workshops- 10
    Guided independent study- 68
    Total hours by term- 98

    Summative Assessment Methods:
    Written exam- 70%
    Sex exercise- 30%

    Other information on summative assessment:
    Formative assessment methods:

    Length of examination:
    One 3-hour examination paper in May/June.

    Requirements for a pass:

    Reassessment arrangements:
    Examination only.
    One 3-hour examination paper in August/September.

Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.


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