Machine Learning

University of Glasgow

Course Description

  • Course Name

    Machine Learning

  • Host University

    University of Glasgow

  • Location

    Glasgow, Scotland

  • Area of Study

    Computer Engineering, 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

  • SCQF Credits

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

    Short Description

    A practical introduction to the foundations of machine learning.


    3 hours per week.

    Requirements of Entry

    Mandatory Entry Requirements Working knowledge of mathematics (e.g., matrices, linear spaces and basic geometry, as covered in, for example, Math1RS or Math1RT).

    Recommended Entry Requirements Some experience in probability and statistics would be useful but is not essential.


    Practical coursework 20% and examination 80%.

    The coursework cannot be redone because the feedback provided to the students after the original coursework would give any students redoing the coursework an unfair advantage.
    Main Assessment In: April/May

    Course Aims

    To present students with an introduction to the general theory of learning from data and to a number of popular Machine Learning methods.

    Intended Learning Outcomes of Course

    By the end of the course students will be able to:

    1:Discuss the principle of learning from data;
    2:Describe the main machine learning methods: regression, classification, clustering, probability density estimation and dimensionality reduction;
    3:Use a selection of common machine learning algorithms and be aware of when one is to be favoured over other;
    4:Implement and use machine learning algorithms in Matlab;
    5:Discuss the major machine learning application area in, for example, Information Retrieval,
    Human Computer Interaction, Bioinformatics and Computer Visions & Graphics;
    6:Detail emerging machine learning approaches such as non-parametric methods and sampling techniques.

Course Disclaimer

Courses and course hours of instruction are subject to change.

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 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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