Undergraduate Course: Neurosymbolic AI (UG) (INFR11306)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course follows the delivery and assessment of Neurosymbolic AI (INFR11303) exactly. Undergraduate and Visiting Undergraduate students must register for this course, while MSc students must register for INFR11303 instead. |
| Course description |
This course follows the delivery and assessment of Neurosymbolic AI (INFR11303) exactly. Undergraduate and Visiting Undergraduate students must register for this course, while MSc students must register for INFR11303 instead.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Machine Learning (INFR10086) OR
Applied Machine Learning (INFR11211)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course follows the delivery and assessment of Neurosymbolic AI (INFR11303) exactly. Undergraduate and Visiting Undergraduate students must register for this course, while MSc students must register for INFR11303 instead.
This course is open to all Informatics students including those on joint degrees.
Students are expected to have background in maths, programming and machine learning. Specifically:
1. Maths: linear algebra, calculus and probability at undergraduate level.
2. Programming: familiarity with a modern programming language such as Python.
3. Machine Learning: Neural network-related concepts e.g. loss functions, hyperparameter tuning, regularisation, gradient descent, etc. More generally, we expect that students have taken a basic introduction to machine learning involving neural networks that covers data processing and exploration, and how to apply and compare baseline methods.
4. Evaluation of machine learning models via e.g. accuracy, precision, recall and ROC AUC.
External students whose DPT does not list this course should seek permission from the course organiser. |
Information for Visiting Students
| Pre-requisites | Students are expected to have background in maths, programming and machine learning. Specifically:
1. Maths: linear algebra, calculus and probability at undergraduate level.
2. Programming: familiarity with a modern programming language such as Python.
3. Machine Learning: Neural network-related concepts e.g. loss functions, hyperparameter tuning, regularisation, gradient descent, etc. More generally, we expect that students have taken a basic introduction to machine learning involving neural networks that covers data processing and exploration, and how to apply and compare baseline methods.
4. Evaluation of machine learning models via e.g. accuracy, precision, recall and ROC AUC. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Supervised Practical/Workshop/Studio Hours 12,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
152 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Feedback |
Sample solutions will be provided to hands-on non-assessed practical exercises. Through the semester, support and feedback will be provided for these assessments and for the material covered in lectures. |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Neurosymbolic AI INFR11303 and INFR11306 | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- judge the effectiveness of core symbolic reasoning approaches in neural network contexts
- describe key neurosymbolic architectures for integrating symbolic and neural approaches
- evaluate and compare neural and neurosymbolic architectures, considering features such as failure modes, safety, trustworthiness, accuracy and explainability
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Reading List
Russell and Norvig, AI: A Modern Approach, 4ed (Global Edition). Pearson. 2022.
Shakarian et al. Neuro Symbolic Reasoning and Learning, Springer, 2023. https://doi.org/10.1007/978-3-031-39179-8
D'Avila Garcez et al. Neural-Symbolic Cognitive Reasoning, Springer, 2009. https://doi.org/10.1007/978-3-540-73246-4
Selected papers will also be part of the reading list. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Neurosymbolic AI,Hybrid AI,Neural networks,Machine learning,Automated reasoning |
Contacts
| Course organiser | Dr Jacques Fleuriot
Tel: (0131 6)50 9342
Email: Jacques.Fleuriot@ed.ac.uk |
Course secretary | Mr Lachlan Boyd
Tel:
Email: lboyd@ed.ac.uk |
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