Introduction to artificial intelligence 4010-WSIa

A detailed description of the subject is provided at the level of a given teaching cycle in the Description section.

Term 2025Z:

1. Introduction to AI - Definition, Applications (3lec).
2. Fundamentals of Machine Learning - Supervised, Unsupervised, and Reinforcement Learning. Essential Mathematics in Machine Learning. (3lec).
3. Unsupervised and Supervised Machine Learning (3lec + 3lab).
4. From Machine Learning to the Perceptron. Shallow Neural Networks (3lec + 3lab).
5. Image Analysis - Classification, Noise Reduction, Segmentation. (6lab).
6. Neural Networks with Embedded Knowledge (3lab)
7. Law and Ethics in AI (3lab).

A total of 15 hours of lectures and 15 hours of exercises.

The order of topics covered and the level of detail covered may vary slightly.

Term 2025L:

A total of 15 hours of lectures and 15 hours of exercises.

The order of topics covered and the level of detail covered may vary slightly.

Term 2026Z:

1. Introduction to AI - Definition, Applications (3lec).
2. Fundamentals of Machine Learning - Supervised, Unsupervised, and Reinforcement Learning. Essential Mathematics in Machine Learning (3lec).
3. Unsupervised and Supervised Machine Learning (3lec + 3lab).
4. From Machine Learning to the Perceptron. Shallow Neural Networks (3lec + 3lab).
5. Image Analysis - Classification, Noise Reduction, Segmentation (6lab).
6. Neural Networks with Embedded Knowledge (3lab).
7. Law and Ethics in AI (3lab).

A total of 15 hours of lectures and 15 hours of exercises.

The order of topics covered and the level of detail covered may vary slightly.

Course coordinators

Jakub Zieliński

Type of course

obligatory courses

Mode

Classroom

Prerequisites (description)

(in Polish) Podstawowa znajomość programowania (Python), matematyki (algebra liniowa, rachunek różniczkowy, statystyka) i logiki.

Learning outcomes

...

Assessment criteria

1. Completion of the course ends with one common grade for all classes (lecture + laboratory) carried out as part of the course.
2. A sick leave does not exempt students from knowledge of the material. It only entitles them to an individualized form of assessment.
3. Students who have received approval for an individualized course of study are required to contact the course coordinator to determine how to achieve all the learning outcomes assigned to the course. If the above-mentioned outcomes cannot be achieved, the coordinator may refuse to grant credit for the course.
4. Attendance is mandatory. In cases of justified absences, the student is required to contact the course coordinator immediately.
5. Regarding the use of artificial intelligence (AI) tools in the preparation of coursework, the rules set out in the "Regulations on the Use of Artificial Intelligence Tools in the Educational Process for the Computational Engineering Degree Programme" apply.
6. Detailed grading criteria are provided at the level of the given academic cycle in the "Notes" section.

Practical placement

Not applicable.

Bibliography

The subject literature has been posted at the level of a given teaching cycle in the Bibliography section.

Term 2025Z:

...

Notes

Term 2025Z:

Students receive credit for the course based on:
- class attendance,
- a written exam, for which the student is eligible after meeting the attendance requirement (two absences are permitted).

Term 2025L:

Students receive credit for the course based on:
- class attendance,
- a written exam, for which the student is eligible after meeting the attendance requirement (two absences are permitted).

Term 2026Z:

Students receive credit for the course based on:
- class attendance,
- a written exam, for which the student is eligible after meeting the attendance requirement (two absences are permitted).