add faq

README.md

@@ -11,19 +11,22 @@
 ## ℹ️ Course information
 
 * Course responsible
-    * Postdoc [Nicki Skafte Detlefsen](https://skaftenicki.github.io/), <[email protected]>
-    * Professor [Søren Hauberg](http://www2.compute.dtu.dk/~sohau/), <[email protected]>
+
+  * Postdoc [Nicki Skafte Detlefsen](https://skaftenicki.github.io/), <[email protected]>
+  * Professor [Søren Hauberg](http://www2.compute.dtu.dk/~sohau/), <[email protected]>
+
 * 5 ECTS (European Credit Transfer System), corresponding to 140 hours of work
 * 3 week period in January
 * Master level course
 * Grade: Pass/not passed
 * Type of assessment: oral presentation + project report
 * Recommended prerequisites: DTU course [02456 (Deep Learning)](https://kurser.dtu.dk/course/02456) or
   experience with the following topics:
-    * General understanding of machine learning (datasets, probability, classifiers, overfitting etc.)
-    * Basic knowledge of deep learning (backpropagation, convolutional neural networks, auto-encoders etc.)
-    * Coding in [PyTorch](https://pytorch.org/). The first day we provide some exercises in PyTorch to
-        get everyone's skills up-to-date as fast as possible.
+
+  * General understanding of machine learning (datasets, probability, classifiers, overfitting etc.)
+  * Basic knowledge of deep learning (backpropagation, convolutional neural networks, auto-encoders etc.)
+  * Coding in [PyTorch](https://pytorch.org/). The first day we provide some exercises in PyTorch to
+    get everyone's skills up-to-date as fast as possible.
 
 ## 💻 Course setup
 
@@ -36,7 +39,7 @@ git clone https://github.com/SkafteNicki/dtu_mlops
 If you do not have git installed (yet) we will touch upon it in the course. The folder will contain all the exercise
 material for this course and lectures. Additionally, you should join our
 [Slack channel](https://join.slack.com/t/dtumlops/shared_invite/zt-1j1zx8t4h-nTbUPibR9xCz58erDyyikw)
-which we use for communication. The link may be expired, write to [me](mailto:skaftenicki@gmail.com).
+which we use for communication. The link may be expired, write to [me](mailto:nsde@dtu.com).
 
 ## 📂 Course organization
 

faq.md

@@ -0,0 +1,52 @@
+# Frequently asked questions
+
+For further questions, please contact [Nicki](mailto:[email protected]).
+
+## Is it possible to attend the course fully online ❔
+
+Mostly yes. All exercises are provided online and lectures will be recorded and streamed. However, do note that
+
+* For project days (see which days in the [time plan](timeplan.md)) you will need to agree with your project group that
+    you are working from home.
+* The oral part of the exam takes place on campus. EuroTEQ students are exempt from this rule. If you have an
+    extremely good reason for not being able to come to campus on the exam date, please contact us within the first
+    week of the course.
+* We have limited TA resources and will be prioritizing students coming to campus for help. If you are attending online,
+    feel free to ask questions on our Slack channel and we will help to the best of our ability.
+
+Overall we try to support flexible learning as much as possible with some limitations.
+
+## What are the prerequisites for taking this course ❔
+
+We recommend that you have a basic understanding of machine learning concepts such as what a dataset is, what
+probabilities are, what a classifier is, what overfitting means etc. This corresponds to the curriculum covered in
+course [02450](https://kurser.dtu.dk/course/02450). The actual focus of the course is not on machine learning models,
+but we will be using these basic concepts throughout the exercises.
+
+Additionally, we recommend basic knowledge about deep learning and how to code in [Pytorch](https://pytorch.org/),
+corresponding to the curriculum covered in [02456](https://kurser.dtu.dk/course/02456). From prior experience, we know
+that not all students have gained knowledge about deep learning models before this course, and we will be covering the
+basics of how to code in PyTorch in one of the
+[first modules](s1_development_environment/deep_learning_software.md) of the course to get everyone up to speed.
+
+## I be missing X days of the course, will that be a problem ❔
+
+Depends. The course is fairly intensive, with most students working from 9-17 every day. If you already know that you
+will be missing X days of the course, then I highly recommend that you go through some of the first sessions before
+the course starts to give yourself a bit of breathing room. If you are not able to do so, please be aware that an
+additional effort may be needed from you to keep up with your fellow students.
+
+## When will the exam take place ❔
+
+The oral part of the exam, which is a small project demo, always falls on the last day of the course. For January 2024,
+this means the 19th. The written part which is a small project report, should be handed in at midnight on the final
+course day.
+
+## Where can I find information regarding the exam ❔
+
+Look at the bottom of this [page](projects.md). Details will be updated as we get closer to the exam date.
+
+## I am a EuroTEQ student, any special rules for me ❔
+
+You will be allowed to attend the oral part of the exam online and we will provide a special Slack channel for you,
+trying to make sure that you get the same help as students from DTU who can attend the course on campus.

mkdocs.yml

@@ -130,3 +130,4 @@ nav:
   - Summary: overview.md
   - Projects: projects.md
   - Challenges: challenges.md
+  - FAQ: faq.md

timeplan.md

@@ -23,7 +23,7 @@ Legend: 📝 Slides, 🎥 Recording.
 !!! note
 
     Current dates listed below are for January 2024 version of the course. The lectures and recordings are currently
-    from January 2024 version of the course. Please note that for January 2024, the first week starts on a Tuesday and
+    from January 2023 version of the course. Please note that for January 2024, the first week starts on a Tuesday and
     ends on a Saturday.
 
 ## Week 1