Course syllabus

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DAT615 DAT615 Neuro-symbolic AI lp2 HT25 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Contact details

For questions about their lectures, contact the respective lecturer. For questions about assignments, contact the TA.

Student Representatives

  • TBD

Course purpose

The purpose of the course is to introduce the students to the field of neuro-symbolic AI. Recent advances in neural networks have quickly moved the state of the art forward for many applications, not least natural language processing and computer vision. But neural models still have weaknesses, for instance when it comes to reliably reasoning logically or planning. This is were methods from symbolic AI (e.g. rule based and heuristic methods) still dominate. The aim of neuro-symbolic AI is to combine the two approaches to get the best from both worlds. It will also allow us to tackle problems which neither method on its own is particularly good at. In this course, we will focus on two application areas: cognitive modeling and languages for emergent communication (with Dubhashi), and program synthesis and mathematics (with Johansson). Towards the end of the course, students should have sufficient knowledge to be able to follow recent research papers in these areas. The final two weeks are dedicated to presentation and discussion of such state of the art research articles. 

Schedule

Please refer to TimeEdit for details of the schedule. Note that the rooms are different each week (for reasons beyond our control). Below is a rough overview of the lectures:

Week   Tuesday 10:00 (Johansson)   Friday 10:00 (Dubhashi)
1 4 Nov Introduction to neuro-symbolic AI 7 Nov Introduction to neuro-symbolic AI from a cognitive science perspective
2 11 Nov Introduction to Program Synthesis 14 Nov Crash course in RL + MCTS
3 18 Nov Neural and Probabilistic Methods for Synthesis 21 Nov RLHF for LLMs, COT and LRM
4 25 Nov Library Learning: Invention of New Concepts 28 Nov Part 1: Intrinsic Motivation in RL
Part 2: Guest lecture by Mateja Jamnik
5 2 Dec AI for mathematics 5 Dec Efficient and Emergent Communication
6 9 Dec Guest lectures by Hazem Torfah and Knut Andreas Meyer 11 Dec Guest lecture by Mantas Baksys and Jonas Bayer on the Kimina prover
7 16 Dec Recent research papers in NeuroAI: Lilo & Stitch 19 Dec Recent research papers in NeuroAI: Poesia, Ma

Office Hours with TAs: Getting help with Assignments

If you want to get help with the assignments you may come to the Office Hours and speak to the TA. They take place from 14:15–15:00 in the EDIT building, room EDIT 5128 (odd weeks) and EDIT 6128 (even weeks):

Week Friday 14:15 Room
1 7 Nov EDIT 5128
2 14 Nov EDIT 6128
3 21 Nov EDIT 5128
4 28 Nov EDIT 6128
5 5 Dec EDIT 5128
6 12 Dec EDIT 6128
7 19 Dec EDIT 5128

Should you have additional questions about the assignments outside these times, please contact Andrea Silvi via email (see contact details above). Canvas direct messages is not preferred. While we of course aim to reply quickly to emails, please note that the TA may need up to 48 hours, so please don't leave it too close to the deadline. You can only expect to get replies during office hours, and not during evenings and weekends (this applies to all teachers).

Course literature

The course literature consist of recent articles from the research literature. Links to reading material related to each lecture will be linked to from the respective lecture page. After the lecture, you will also find copies of the slides there.

Course design

The course has two lectures per week. The lectures will introduce topics from both symbolic AI, machine learning and neuron-symbolic AI. Students are expected to attend lectures and are encouraged to read the material linked from each lecture page. To contact the teachers, please use email. There might be a delay in getting a reply to messages sent in Canvas.

There will be three hand-in programming assignments and a presentation of a research paper. These should be done in pairs and the programming assignments should be submitted via Canvas by the deadline stated. They are not strictly mandatory, but give bonus points towards the final grade (see below under Examination for details). Assignments handed in before the deadline will be graded. There will be no second submissions. Deadlines are firm, unless granted an extension by the examiner for valid reasons such as illness. Vacation trips, failure to read the deadline dates etc does not count as valid reasons. It is the student's responsibility to keep themselves informed about the course by reading updates and announcements on the course Canvas page frequently.

Dates for the assignments are:

Week Assignment Deadline/Presentation
3 Programming Assignment 1 Fri, 21 Nov (at 23:59)
5 Programming Assignment 2 Tue, 2 Dec (at 23:59)
5 Select Research Paper for Presentation Fri, 5 Dec (at 23:59)
7 Research Paper Presentations (Session 1) Wed, 17 Dec (8:00-10:00 in EL51, EL53)
7 Research Paper Presentations (Session 2) Fri, 19 Dec (8:00-10:00 in FL61, FL62)
8 Programming Assignment 3 Tue, 23 Dec (at 23:59)

Changes made since the last occasion

The following changes have been made since the last occasion (HT24):

  • The lectures have been updated to reflect new research in the field of neuro-symbolic AI.
  • The computation of the final grade based on homework assignments and exam points has changed slightly.

Learning objectives and syllabus

Learning objectives:

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.

Link to the course syllabus.

Examination form

The course will have a hall exam in January and a re-exam in August. No aids are allowed. The exam is compulsory.

The programming assignments, paper presentation and attendance of another presentation, where you act as an opponent, count for a maximum of 6 bonus points:

  • Each programming assignment gives max. 1 bonus point, based on your score on the assignment (divided by 10).
  • The research paper presentation gives max. 3 bonus points, based on the following criteria (judged by the teacher):
    • You must demonstrate that you understand the main research question and results of the presented paper.
    • You must present the material clearly and using appropriate audio-visual aids.
    • Your answers to the opponent's questions must demonstrate a good understanding of the paper.
    • As the opponent, you must ask insightful questions about the presentation, demonstrating that you understood that other paper as well.

The final grade is calculated based on a combination of the final exam score and the bonus points obtained from the programming assignments and the presentation:

  • Your score e on the exam (max. 60 points).
  • Your total number of bonus points p from the assignments and paper presentation (max. 6 points).
  • Your final score s = min(e+p, 60).
  • Grades will be based on the final score s: 3 (28 points), 4 (38 points), 5 (48 points).

Note that:

  • Assignments and presentations are not mandatory but strongly recommended for understanding!
  • Bonus points (max 6) are only valid for exams in this course round, i.e the January 2026 and August 2026 exams, not for any subsequent re-exams in later years.

Examination dates and locations: Please refer to this page.

 

 

Course summary:

Course Summary
Date Details Due