Morten Hjorth-Jensen
84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf-8
|
|
|
|
# <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
|
|
# doconce format html exercisesweek47.do.txt -->
|
|
# <!-- dom:TITLE: Exercise week 47 -->
|
|
|
|
# # 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?
|