Course syllabus for Research-oriented course in data science and AI

The course syllabus contains changes
See changes

Course syllabus adopted 2022-02-01 by Head of Programme (or corresponding).

Overview

  • Swedish nameForskningsinriktad kurs inom data science och AI
  • CodeDAT530
  • Credits7.5 Credits
  • OwnerMPDSC
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering, Mathematics
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 87119
  • Block schedule
  • Open for exchange studentsNo

Credit distribution

Module
Sp1
Sp2
Sp3
Sp4
Summer
Not Sp
Examination dates
0122 Project 7.5 c
Grading: TH		7.5 c

    Examiner

    Go to coursepage (Opens in new tab)

    Course round 2

    • Teaching language English
    • Application code 87122
    • Open for exchange studentsNo

    Credit distribution

    Module
    Sp1
    Sp2
    Sp3
    Sp4
    Summer
    Not Sp
    Examination dates
    0122 Project 7.5 c
    Grading: TH    		 7.5 c

      Examiner

      Go to coursepage (Opens in new tab)

      Course round 3

      • Teaching language English
      • Application code 87120
      • Open for exchange studentsNo

      Credit distribution

      Module
      Sp1
      Sp2
      Sp3
      Sp4
      Summer
      Not Sp
      Examination dates
      0122 Project 7.5 c
      Grading: TH      		 7.5 c

        Examiner

        Go to coursepage (Opens in new tab)

        Course round 4

        • Teaching language English
        • Application code 87121
        • Open for exchange studentsNo

        Credit distribution

        Module
        Sp1
        Sp2
        Sp3
        Sp4
        Summer
        Not Sp
        Examination dates
        0122 Project 7.5 c
        Grading: TH        		 7.5 c

        In programmes

        Examiner

        Go to coursepage (Opens in new tab)

        Eligibility

        General entry requirements for Master's level (second cycle)
        Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

        Specific entry requirements

        English 6 (or by other approved means with the equivalent proficiency level)
        Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

        Course specific prerequisites

        The student shall have a Bachelor's degree in a subject that is relevant for the MPDSC master programme.

        In addition, a mandatory pre-requisite to take this course is that the student has established a contact with the examiner of the course; it is up to the examiner to decide whether a student is accepted or not to the course.

        Aim

        This course will deal with an area of current interest in data science and AI.

        The aim of this course is to give the student the chance to follow, for example, a graduate course or a lecture series by a visiting researcher at the department.

        Learning outcomes (after completion of the course the student should be able to)

        The course shall give the student further knowledge in a research area of relevance to data science and AI.

        Knowledge and understanding
        • Master the terminology, concepts and theories associated with the selected area;
        • Demonstrate deep knowledge and understanding in the area of the course, and insight into current research and development;
        • Demonstrate deep methodological knowledge in the area of the course;
        Skills and abilities
        • Demonstrate the ability to critically and systematically integrate knowledge and to analyse, assess, and deal with complex issues in the area of the course;
        Judgement and approach
        • Search for, and extract, necessary information from scientific publications in the selected area of the course, with the purpose of identifying strengths and weakness of solutions, approaches and methodologies.

        Content

        Research-oriented course in a field of relevance to data science and AI.
        The particular content will be determined prior to each instance of this course and will be made available on the course home page

        Organisation

        The organisation of the course may include lectures, tutorials, seminars, and/or labs and supervision in conjunction with these.

        Literature

        The course literature will be published on the course home page.

        Examination including compulsory elements

        The examination might vary on each instance of the course. It might consist of a written or take home exam, assignments, presentation of work in a seminar, or a combination of these.
        Further information will be given on the course home page.

        The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.

        The course syllabus contains changes

        • Changes to course rounds:
          • 2023-11-20: Examinator Examinator changed from Birgit Grohe (grohe) to Marina Axelson-Fisk (marinaa) by Viceprefekt
            [Course round 3]
          • 2023-11-17: Examinator Examinator changed from Birgit Grohe (grohe) to Marina Axelson-Fisk (marinaa) by Viceprefekt
            [Course round 4]
          • 2023-05-16: Block Block A added by Simon Olsson
            [Course round 1]