Exams in France

Overview

Instead of a big table of mostly similar requirements, below is an overview highlighting the key differences:

• Homework/projects that account for 30% of the grade:

  • Statistics, Data Mining: mandatory; after the week with lectures and exercise sessions

  • Neutrino Physics: optional; overlaps with the big exam phase

• Allowed aids:

  • “the default”:

    • Lecture materials (analog+digital)

      • Interpretations of what counts as lecture material vary a lot.

      • Possibly with specific exceptions, such as electron scattering, Z decay, …

    • Calculator (hardly needed)

    • Possibly the PDG (booklet or online)

  • Quark-Gluon-Plasma, Data Mining, Deep Learning: no aids at all

  • Symmetries, Cosmology: analog lecture materials only

  • Python, Machine Learning (on a PC): Documentation inside the IDE and online; possibly Jupyter notebooks from the exercise sessions

FAQ

• Do I need to bring anything?

  • Nothing but your notes (if allowed), a pen, and (though not always checked) your student ID card

  • Blank paper will be provided

• On what scale will I be graded?

  • The French scale ranges from 0 (worst) to 20 (best).

  • You pass if your average on all the semester's exams is ≥10 points.

• Are there any exams from previous semesters for practicing?

  • Yes, usually: Ask the professors and look at the courses' Moodle pages

  • There is also an online collection about here. In order to protect the intellectual property, I can't share the actual address publicly.

• (How) are grades normalized?

  • At the end of the semester, there will be a conference of all teaching staff where the grades can be shifted, apparently without adhering to specific rules.

Structuring of modules and weighting of courses

The UCA seminar on particle physics is not listed in the releve de notes, since it is not graded, but its 3 CP do count.

• Quantum Field Theory, Gauge Theories & Quant. ChromoDyn. → 6 CP

  • Quantum Field Theory (Coef. 0,45)

  • Quantum ChronoDynamics (Coef. 0,35)

  • Gauge Theories (Coef. 0,20)

• Foundations of the Standard Model of Particle Physics → 9 CP

  • Symmetries (Coef. 0,30)

  • Introduction to particle Physics (Coef. 0,30)

  • ElectroWeak Standard Model (Coef. 0,40)

• Programming & data analysis → 6 CP

  • Programming (Coef. 0,50)

  • Data structures and mining (Coef. 0,50)

• Statistics and artificial Intelligence → 6 CP

  • Statistics (Coef. 0,50)

  • Machine Learning (Coef. 0,50)

• Guest lect. on various topics (related to particle phys.) → 3

  • General Relativity / Cosmology