Course syllabus
Course memo
The course is offered by the department of Mechanics and Maritime Sciences.
General information
The course will be given only on-site at Chalmers. There will be no remote (Zoom) lectures. More information will follow in the first lecture.
Contact details
NOTE: The contact information below applies from Jan. 14 and onwards.
Between Dec. 22 and Jan. 13, please do NOT (repeat: do NOT) send any e-mails - I will not read them anyway (in that period), and I want to minimize the backlog (typically hundreds of e-mails) that I must go through after the Christmas vacation. E-mails received before Jan. 14 may not be read at all (or much later), but from Jan. 14 and onwards I will read and respond to any (new) incoming e-mails from students.
If you have questions regarding the schedule of the course, please see below and TimeEdit.
If you have questions, of any kind, regarding admission to the course, please contact the administration - teachers are not involved in course admissions.
if you have other questions regarding, for example, prerequisites, programming languages, examination, course literature, mandatory sessions, re-examination (for students who took the course given last year) and so on, I'll be happy to answer them AFTER Jan. 14, but not before.
During the course, we strive to be available as much as possible. You are welcome to ask questions at any time, for example in the lectures (and you may also ask questions via e-mail or telephone). You are always welcome at our offices. You do not need to make an appointment, but since we are not always in our offices it's a good idea to first check that we are there (via e-mail or telephone).
Lecturer and examiner:
Professor Mattias Wahde, tel.: 031 772 3727, e-mail: mattias.wahde@chalmers.se
Course assistants:
Minerva Suvanto, e-mail: minerva.suvanto@chalmers.se
Vivien Lacorre, e-mail: vivien.lacorre@chalmers.se
Finding our offices: Go to Hörsalsvägen 7, enter the building (nya M-huset), so that you have Café Bulten on your right as you enter. Then go up one flight of stairs, and enter the corridor (Vehicle Engineering and Autonomous Systems). If the door is locked, please dial the appropriate number, as shown in the list beside the door.
Course purpose
The aim of the course is for the students to gain knowledge regarding interpretable methods in artificial intelligence, as well as applications of such methods, especially in high-stakes situations, for example in healthcare, automated driving, finance, and so on. The course also aims to highlight differences between interpretable systems and so-called black-box models, e.g., deep neural networks. Ethical aspects of AI are also covered.
Schedule
The course schedule is given below. The lectures can also be found in TimeEditLinks to an external site..
| Date | Room | Time | Content |
| 20260120 | HC2 | 08.00-09.45 | Course introduction and motivation; Brief description of the topics covered in the course. |
| 20260121 | HA3 | 13.15-17.00 | Black-box AI and interpretable AI (description and comparison). Neurosymbolic models. AI ethics, Brief introduction to Python for AI. |
| 20260127 | HC2 | 08.00-09.45 | Black-box architectures (neural networks): Deep neural networks (DNNs), e.g., convolutional neural networks (CNNs), large language models (LLMs) |
| 20260128 | HA3 | 13.15-17.00 | Interpretable models: Linear models (linear perceptrons), linear and logistic regression, Bayesian methods, k-nearest neighbour methods, decision trees, symbolic regression |
| 20260203 | HC2 | 08.00-09.45 | Time series prediction, Handout of assignments (projects) |
| 20260204 | HA3 | 13.15-17.00 | Data classification (images, text) |
| 20260206 | HC2 | 08.00-09.45 | Natural language processing (I) |
| 20260211 | HA3 | 13.15-17.00 | Assignment work session (assistants available as tutors in the classroom) |
| 20260217 | HC2 | 08.00-09.45 | Natural language processing (II) |
| 20260227 | HC2 | 08.00-09.45 | Assignment work session (teacher and assistant available as tutors in the classroom). |
| 20260303 | HC2 | 08.00-09.45 | Assignment work session (teacher and assistant available as tutors in the classroom). |
| 20260304 | --- | --- | No lecture! |
| 20260310 | HC2 | 08.00-09.45 | Assignment work session (teacher and assistant available as tutors in the classroom) |
| 20260311 | HC1 | 13.15-17.00 | Assignment presentations (mandatory attendance). Handin of assignments |
Course literature
The course literature will consist of lecture notes (slides and other notes), links to various scientific papers, and web resources. This material will be provided gradually during the course. All course material will be provided (free of charge) on the Modules page.
Course design
The course starts with a few weeks of lectures and some practical activities (preparing for programming work later). Assignments will be handed out in Study week 3 (and should be handed in on the day of the final session, March 12). From Study week 4 and onwards, there is a mix of lectures and work sessions - in the latter, the students are expected to work on their assignments (that will involve Python programming). All assignments are solved individually (there is no group work). In the final session (March 12) we will have presentations of the assignments, where each student is given a few minutes to present their work.
Changes made since the last occasion (2025)
The course is completely new (given for the first time), but it has a certain overlap with the course that preceded it (Intelligent Agents), in particular regarding the parts that deal with natural language processing.
Learning outcomes
Learning objectives:
- Define and contrast, on the one hand, black-box models and, on the other, interpretable (glass-box) models in artificial intelligence (AI)
- Define and describe neuro-symbolic models and methods
- Discuss and compare different kinds of AI applications
- Select a suitable model class for a given application
- Define, implement, and train AI-models (both black-box models and interpretable models) for different applications, e.g., in natural language processing (NLP), data classification, image processing, time series prediction, autonomous robots, and so on.
- Discuss various ethical aspects related to artificial intelligence
Examination
Examination: There will be one assignment, with several parts (some of which are mandatory whereas other are voluntary), worth a total of 100 p. More details (for example, grade requirements) will follow when the course starts.
Course summary:
| Date | Details | Due |
|---|---|---|