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# # Exercise week 47
# **November 20-24, 2023**
# 
# Date: **Deadline is Sunday November 26 at midnight**

# # Overarching aims of the exercises this week
# 
# The exercise this week is a simple course survey and feedback.  This
# is important for us in order to improve our teaching material, the
# active learning format and anything else related to a succesful
# mastering of central machine learning methods and their applications.

# ### Why did you choose this course?

# ### What was your programming knowledge before you started?
# 
# And do you feel this course added to your programming competences and skills?

# ### How do you judge your own level of knowledge on machine learning before and after this course?
# 
# Here you can discuss your level of skill/knowledge at start of course
# and at the end of the course and how these matched the level of
# skill/knowledge needed to complete the projects.

# ### Did the projects and the teaching material allow you to deepen your insights about Machine Learning?
# 
# Feel free to comment here.

# ### Project based teaching and active learning
# 
# This is a project based course and we as teachers would like to keep
# it as it is since we see very clearly that people who attend this
# course have a very good learning outcome. Project based courses are
# however demanding (and expensive seen from the university admin) when
# it comes to proper feedback and evaluations. Feel free to discuss
# whether you found a project-based and active learning approach
# useful. Feel also free to comment upon things we can improve upon or
# alternative ways to assess whether the learning outcomes have been
# achieved. Would you for example a standard 4 hours written exam
# be something you would prefer?  Or other alternatives to projects? We
# would very much value your thoughts here since projects are an
# essential part of this course.

# ### Usefulness of the weekly exercises
# 
# Did the weekly exercises help in getting started with the projects?
# How relevant where they for solving the projects?  Feel free to
# elaborate

# ### Active learning/lab sessions and lectures
# 
# Was there a good link between lectures and active learning sessions?
# Would you prefer an active learning environment only with no lectures
# or would you prefer a more lecture based format with lab sessions only
# (that is no discussion at the beginning of the lab sessions)? Feel
# free to comment.

# ### How would you improve this course?
# 
# Are there topics which are missing, topics which could have been
# omitted and/or discussed in more depth? Feel free to add your comments
# here such as how to improve to teaching material and more.

# ## Then some basic questions

# ### Which is your preferred information chanel, Canvas, Discord, mail or other?

# ### Was the weekly update with plans etc useful?

# ### Was it easy to access the course material?

# ### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc

# ### If you did not attend the lectures or the active learning/lab sessions, which resources did you use?

# ### Any other topics, impressions, ideas etc you would like to share with us?