Course grading and exams

Course grading will follow preferentially a modality comprising in-itinere assignments and a final oral exam. In-itinere assignments waive the final project.

Midterms are only available to students regularly following the course: mechanism to control attendance will be in place. Students who don't follow regularly the course can use the traditional exam modality.

Midterm Assignments

Midterms consist of interim coding assignments involving a quick and dirty (but working) implementation of models (e.g. colab notebook) introduced during the lectures (with and without the use of supporting deep learning libraries).

There will be 3 interim midterms, which will have to be developed individually, roughly aligned with the conclusion of the major modules of the course (expect midterms to be scheduled roughly every 4 weeks). 

There will also be a final assigment (midterm n. 4) which will consist in a presentation of a recent research paper on topics/models related to the course content. This final assignment will be executed in groups.

Coding midterms will be automatically tested for correctness but not scored. During the final assignment the instructors will ask questions about the paper to determine knowledge of the paper: again no score provided, only pass/fail.

Oral Exam

The oral examination will test knowledge of the course contents (models, algorithms and applications).

Exam Grading (with Midterms)

The final exam vote is given by the oral grade. The midterms only wave the final project but do not contribute to the grade. In other words you can only fail or pass a midterm. You need to pass all midterms in order to succesfully wave the final project.

Traditional Exam Modality (No Midterms / Non attending students)

Working students, those not attending lectures, those who have failed midterms or simply do not wish to do them, can complete the course by delivering a final project and an oral exam.  Final project topics will be released in the final weeks of the course: contact the instructor by mail to arrange choice of the topics once these are published.

The final project concerns preparing a report on a topic relevant to the course content or the realization of a software implementing a non-trivial learning model and/or an AI-based application relevant for the course. The content of the final project will be discussed in front of the instructor and anybody interested during the oral examination. Students are expected to prepare slides for a 15 minutes presentation which should summarize the ideas, models and results in the report. The exposition should demonstrate a solid understanding of the main ideas in the report.

Grade for this exam modality is determined as

 \( G = 0.5 \cdot (G_P + G_O) \)

where \( G_P \in [1,30] \) is the project grade and \( G_O \in [1,32] \) is the oral grade