.. Example documentation master file, created by sphinx-quickstart on Sat Sep 23 20:35:12 2023. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Advanced Neural and Cognitive Modelling 2025 =================================== This website contains the materials for the lab sessions of the couse Advanced Neural Cognitive Modelling 2025. The TA for these labs is `Anna Bavaresco `_. Please reach out to her if you have any questions related to the materials on this website. .. note:: The content of this website is largely based on materials from the `2022 edition `_ of the couse, which were curated by `Marianne de Heer Kloots `_ and `Ashley Burgoyne `_. +++++++++++++++ Grading system +++++++++++++++ Each of your lab submissions can be assigned a maximum of 2 points and the overall grade will be an average of the individual scores. If you are not satisfied with the grade of one (or more) of your assignments, you have the possibiliy of resubmitting them. This will not completely erase your previous grade(s), but the new grade(s) for your assignment will be the average between the old and the new grade(s). When converting the final points (mean of all the four assignment) to an actual grade, a 0 will be converted to a 4, a 1 to 6, and 2 to 10. Each assignment will be graded acconding to the following criteria: #. **Code**: The extent to which your code is correct, well-structured, and well-commented #. **Motivation**: The logic of the explanations and motivations behind the conclusions drawn from the assignment. Note that, while the "correctness" of the conclusions matters, full points will not be given if such conclusion is not motivated well. At the same time, partial points will still be given if an unexpected conclusion is accompanied by insighful speculations. #. **Delivery**: This assessed whether contents are expressed clearly, concisely, and using appropriate terminology. Assignments should be written following best practices for academic writing — in a nutshell, avoid unnecessarily complex syntax and jargon, but _do_ use precise terminology and avoid colloquialisms. For each of the above criteria, you will receive a score ranging from 0 to 2: * **0** (converted to 4): Assignment not turned in without a valid motivation. * **0.5** (converted to 5.5): Code just drafted, delivery poorly curated, motivation inconsistent. * **1** (converted to 6): Code present and running but not very well commented/motivated, content understandable but often presented using inappropriate/colloquial terms and structures, the main ideas are roughly present but not justified/explained well. * **1.5** (converted to 8): Code nicely written and including the required steps, with only minor mistakes; effective delivery which might benefit from minor improvements, solid motivation but not fully supporting the expected conclusion. * **2** (converted to 10): good and well-documented code including all the expected steps, nice academic writing style with appropriate terms, solid and well-explained motivations. .. note:: The assignments are not perfectly balanced in the amount of coding and/or writing they require. When averaging the scores for the aforementioned criteria, their prominence in the assignment will also be taken into account. +++++++++++++++ Contents +++++++++++++++ .. Section 1: Week 1 .. 1_Logistic_regression_for_musical_tags.ipynb .. Section 2: Week 2 .. 2A_Language_Model_Refresher.ipynb .. 2B_RSA_with_fMRI_Data.ipynb .. 2C_LM_Surprisal_and_EEG_data.ipynb .. toctree:: :maxdepth: 1 :caption: Week 1: week_1/1_Logistic_regression_for_musical_tags.ipynb .. toctree:: :maxdepth: 1 :caption: Week 2: week_2/2A_Language_Model_Refresher.ipynb week_2/2B_RSA_with_fMRI_Data.ipynb week_2/2C_LM_Surprisal_and_EEG_data.ipynb .. toctree:: :maxdepth: 1 :caption: Week 3: week_3/3_IRT_Stan.ipynb .. toctree:: :maxdepth: 1 :caption: Week 4: week_4/4_Vision,_Convolutions_and_Recurrence.ipynb .. toctree:: :maxdepth: 1 :caption: Project Inspiration: project_ideas.ipynb +++++++++++++++ FAQs +++++++++++++++ **What do I have to submit? A notebook, a PDF, or both?** You should submit both a notebook and a PDF. The notebook should include the code to reproduce your analyses/results. As for the PDF, please include there any written answer/report that the assignment/question require. In addition, consider including some figures or tables, if you think it might be convenient to present your results more effectively. Tables and figures will not count toward the page/word limit (if there is any). **When will I see the grade for my assignment?** Normally, the TA aims to publish grades for one assignment before the submission deadline of the next one. **Can I work on the notebooks together with coursemates?** While assignments are submitted and graded **individually**, you are allowed to discuss answers and solutions with coursemates. Note that the goal should always be to learn contents better. Copying code or textual answers from your classmates will result in a deduction of points from your grade. **Can I use AI to help me with the notebooks?** Yes—but responsibly. AI should be a learning tool, **not** a shortcut to submit the best possible report with the minimum effort. Note that using LLMs to improve the writing style of your report is also discouraged; writing effectively is part of the skills that should be learnt in a research Master, not some add-on that can be delegated to AI. **Due to unforeseeable circumstances, I will be unable to submit the assignment(s) within the deadline. What should I do?** Contact the TA or the course coordinator. If you have good reasons for being unable to submit the assignment, they will most likely understand and do their best to find a solution that is suitable for you. **I have accidentally submitted the wrong PDF/notebook. Will this result in a deduction of points?** Yes. Unfortunately, it is impossible for TAs to tell honest mistakes from skeaky attempts to get deadline extensions. In addition, ensuring the submitted files are the correct ones is part of what distinguishes a good assignment from a sloppy one. **Is computer lab attendance mandatory?** Attending computer labs is not mandatory, so no need to worry if you have schedule conflicts. If you don't, however, consider attending the computer labs in person, as it will make it easier for you to ask clarification questions to the TA(s) and/or discuss any doubts you may have with coursemates.