Course syllabus for Neuro-symbolic AI

The course syllabus contains changes
See changes

Course syllabus adopted 2024-02-08 by Head of Programme (or corresponding).

Overview

  • Swedish nameNeuro-symbolisk AI
  • CodeDAT615
  • Credits7.5 Credits
  • OwnerMPALG
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 02136
  • Maximum participants50 (at least 10% of the seats are reserved for exchange students)
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0124 Examination 7.5 c
Grading: TH		0 c7.5 c0 c0 c0 c0 c
  • 14 Jan 2025 pm J
  • 16 Apr 2025 pm J
  • 20 Aug 2025 pm J

In programmes

Examiner

Information missingGo 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

To be eligible for the course, a student must have passed a minimum of the following courses:
  • 7.5 credits of programming (Python experience desirable but not absolutely required)
  • 7.5 credits of basic machine learning (for example TDA233, MVE440, DAT340)

Aim

The aim of the course is to introduce students to the basic concepts in symbolic, neural and neuro-symbolic AI. This includes the respective strengths and weaknesses of the different methodologies, as well as how neuro-symbolic methods can benefit from both sides.

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

After completion of the course the student should be able to:
  • Separate what characterise symbolic and neural AI.
  • Explain what neuro-symbolic AI encompasses.
  • Apply and implement methods and algorithms for neural AI
  • Apply and implement methods algorithms for symbolic AI.
  • Apply and implement neuro-symbolic AI methods and algorithms.

Content

The course is divided under three headings:
  • Neural AI methods, for example reinforcement learning.
  • Symbolic AI methods, for example for program synthesis.
  • Neuro-symbolic AI, specifically how neural and symbolic methods and systems can be combined to take advantage of each other's strengths.

Organisation

The course will consist of weekly lectures.

In addition, there will be non-obligatory written and programming assignments to do at home. Students are however strongly encouraged to also do the exercises as these may count as extra points towards the final grade.

Literature

Research papers uploaded to the course page

Examination including compulsory elements

The course has a written final exam. Homework assignments are non-obligatory but could generate bonus points towards the final grade.

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 on educational support due to disability.

The course syllabus contains changes

  • Changes to course rounds:
    • 2024-05-06: Block Block changed from C to D by Moa Johansson
      [Course round 1]
    • 2024-04-30: Block Block C added by Elin Johansson
      [Course round 1]