Abschnittsübersicht
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1. Overview
This course will mainly address two different problems in a continuous manner:
- In its first part, we will consider the problem of how to model uncertainty and how to make decisions from uncertainty models. We will start from probabilities and will then proceed to more complex models.
- In the second part, we will consider the problem of quantifying uncertainties and explanations in learning problems, and more particularly in the prediction part of learning problems.
We shall also provide illustrations of how to use the mathematical elements of the lectures in applications, such as large language models, Out-of-Distribution detection, and so on.
2. Assessment
As requested by UTC, we will perform two types of evaluations.
- The first evaluation will take the form of a mini exam consisting of multiple-choice questions, which are related to either notions given during the lectures or (simple) exercises that are similar to the ones used to illustrate the content of the lectures.
- The second evaluation will take the form of a two-round (Jigsaw) group assignment, with the constraint that the roles of the group members should be different for each round.
2.1. First evaluation: Details
The first evaluation will take the form of a mini exam. There will be 6 questions, and the students will have up to 20 minutes for the mini exam. The mini exam will be given at the beginning of Lecture 6. This will contribute to 20% of the final evaluation.
2.2. Second evaluation: Details
The second evaluation will take the form of a two-round (Jigsaw) group assignment, with the constraint that the roles of the group members should be different for each round. For each round, each group of 3 students will have 15 minutes for the presentations/illustrations, followed by at most 15 minutes (at most 10 minutes for the first round and at most 15 minutes for the second round) for the question-and-answer session, during the last two lessons of AOS4. The first and second round presentations will contribute, respectively, 20% and 60% to the final evaluation.
For this evaluation, groups will have to choose one of the following assignments (To be revised):
- Short lecture or "being in a teacher's shoes". In this case, the group should create a lecture focusing on a topic we did not cover in class, that can either concerns uncertainty modeling and uncertainty in learning problems. What we expect as a result of such a choice is the following:
- A short explanation (in .pdf) of the topic and its merits, followed by your (short) reflections on pedagogical aspects of your illustration.
- A set of slides to be used during the lectures, and additional possible pedagogical material (notebooks, etc.). Such slides should clearly be intended as a lecture on the topic.
- A lecture where the group will act as teachers to deliver a short course on a specific topic, which can be accompanied by live demonstration, illustration, or anything that will make the course easy to follow for other students
- Tutorial or “wake up the blogger in you”. In this case, each group will have to make a tutorial or a blog post (in the style one can find in Kaggle or towards data science) about a learning method. What we expect as a result of such a choice is the following:
- The implementation of a method.
- A way to easily test and understand the method: this can be a notebook, a README file to execute, etc.
- A short explanation (in .pdf, as a blog post) of the method and its merits, followed by your (short) reflections on pedagogical aspects of your tutorial.
- An offline tutorial (in the style of towards data science/kaggle), possibly with an accompanying notebook
- Paper illustration or "explain to your high-school nephew". In this case, each group will take a paper and will have the task to illustrate/explain a part of the paper through a media of their choice: it can be a presentation, a video, a poster, a live demonstration/exercise, an interactive website, etc. The illustration/explanation should be pedagogical, in the sense that it should be accessible to a non-expert (who does not know advanced math or computing). What we expect as a result of such a choice is the following:
- A short explanation (in .pdf) of the method and its merits, followed by your (short) reflections on pedagogical aspects of your illustration.
- Supporting materials (if there are any), such as a video, a poster, a live demonstration/exercise, an interactive website, etc.
- A pedagogical illustration of a paper topic (not especially illustrating the whole paper, but at least making a part of the paper understandable to a wide audience).
Each group is recommended to choose one paper from the suggested list as the key reference for the assignment. The groups should choose different key references, ie, no reference should be chosen by more than 1 group.
The groups are recommended to look at related works in the literature to enrich the content of the presentations/illustrations. Depending on the size and complexity of the chosen paper, not all of it has to be explained/illustrated. It is better to focus on a specific part and be really pedagogical/illustrative than trying to show too much and be confusing.
The two rounds: Details
- First round (Lecture 6): The groups should
- submit the required materials in advance
- give their presentations/illustrations
- take into account the feedback from other students and teachers to prepare for the second round
- Second round (Lecture 7): The groups should
- submit the revised materials in advance
- give their revised presentations/illustrations
NOTE: In the revisions of the short explanations, which should be submitted as part of the second round, each group should add a (short) reflection on the following points:
- The benefits (if you think there are any) of the feedback received from the first round
- The benefits (if you think there are any) of the discussions with other members, such as the one who takes care of your role during the first round.
Teachers
- Vu-Linh Nguyen, Heudiasyc laboratory