Duration
40h Pr
Number of credits
| Bachelor of Science (BSc) in Computer Science | 3 crédits |
Lecturer
Substitute(s)
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
In this course, groups of 2 or 3 students program applications that apply the concepts studied in physics and mathematics courses.
The aim is to facilitate the understanding and assimilation of these subjects through hands-on, concrete applications.
The programming environment is the same as that used in the first computer science project (INFO2056-1), namely a Python environment.
The programmed applications cover the following topics:
- Electric and magnetic fields
- Coulomb force
- Laplace force
- Ohm's law
- RC circuits
- Direct current vs alternating current (AC vs DC)
- Direct current motor (DC motor)
- Boolean algebra
- Basic digital electronics
Learning outcomes of the learning unit
By the end of the course, students will have learned how to apply concepts studied in physics and mathematics to concrete problems. They will be capable of implementing these concepts in Python to model, illustrate, or solve physical and mathematical problems, and of orally defending their implementation choices and problem-solving methodology.
Prerequisite knowledge and skills
Students must be comfortable with the programming environment used in this course (relying on Python's Pygame module). Foundational courses in physics and mathematics must have been completed, or be taken concurrently.
Planned learning activities and teaching methods
The course is organized as weekly laboratory sessions (4h/week x 13 sessions), supervised by professors, teaching assistants, and student tutors. Part of the preparation and programming work may also be carried out at home.
Attendance policy: Attendance at weekly lab sessions is strictly mandatory until all 9 lab assignments have been both submitted online AND successfully presented to an evaluator (professor, assistant, or tutor). Once a group has had all 9 labs validated by an evaluator, members of that group are exempt from attending the remaining sessions of the semester.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Mandatory weekly lab sessions: 4h/week ×13 sessions (until all 9 labs have been evaluated and approved).
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
- Physics and mathematics course materials.
- Course web page containing detailed laboratory guidelines and instructions.
- eCampus page with supplementary pedagogical and remediation materials.
Continuous assessment
Further information:
Continuous assessment
Further information:
The course grade is individual and determined as follows.
Laboratories (18 points):
There are 9 programs in total, each graded out of 2 points (9×2=18 points).
Each program must be submitted on time on the specified platform (eCampus or Gradescope) AND defended in person during a lab session before an evaluator, who verifies code execution and individual understanding. Any submitted code that is not presented or explained will not be validated. Unexcused late submissions receive a grade of 0.
Individual oral quiz (3 points):
A short individual oral examination conducted during one of the laboratory sessions. The date and topics of this quiz are announced at the beginning of the semester.
Absence penalties:
-1 point per unexcused absence from mandatory sessions.
Important notes: Mandatory attendance: Attendance is required at every session until all 9 laboratories have been submitted and validated by an evaluator. Students with 4 or more unexcused absences will be awarded a grade of "Absent" (A) for the entire course.
Generative AI policy:
The core learning goal of this unit is the personal acquisition of algorithmic reasoning, modeling, and programming skills. The use of generative Artificial Intelligence tools (such as ChatGPT, GitHub Copilot, Claude, Gemini, or similar) to write, solve, or debug assignments is strictly prohibited.
Evaluators systematically question students individually on their code: each student must be able to explain, justify, and modify their code on request. Any unauthorized use of AI constitutes academic misconduct (plagiarism) and will be penalized according to ULiège general study and examination regulations.
Examination periods: There are no formal exams in June or August (no second session). Assessment is based solely on continuous assessment during the semester.
Work placement(s)
Organisational remarks and main changes to the course
- The course takes place in the computer labs of the Mathematics Institute (Building B37).
- Grading is based exclusively on continuous assessment during the semester; there are no exams in either June or August.
Contacts
- Bertrand Cornélusse, bertrand.cornelusse@uliege.be