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  • FAQ

    1. I have a problem, who I should contact?

    If you have questions about the course content or problems with coding exercises, please send email to cs-ej3211@aalto.fi or use Slack (invite).

    2. There are too many platforms in this course, I am lost.

    1. The main course page is MyCourses Aalto page. There you can find all information about the course and links to other resources. 
    2. We will be running our Python code (in a form of Jupyter notebooks) in Aalto Jupyter Hub. In order to get access to the course material, you should login with your Aalto account and choose server option: "CS-EJ3211 Machine Learning with Python (2023)".
    3. Third important platform is Slack, where teachers and students can easily communicate, post questions and discuss any related topics. You can join Slack with this invite. Instructions how to join slack channels are here.
    4. You can login to Zoom with  Aalto account (select sign in with SSO -> Your company domain = aalto)

    3. How to use JupyterHub?

    1.  Go to https://jupyter.cs.aalto.fi/
    2.  Login with your Aalto account
    3.  Select server option "CS-EJ3211 Machine Learning with Python (2023)"
    4.  Go to "Assignments" tab and fetch available assignment (jupyter notebook). Now you should see the folder with notebook under the "Files" tab.
    5.  Go through the notebook, read material carefully and complete coding exercises.
    6.  Submit your notebook BEFORE the deadline by clicking "submit" button.
    7.  You will be able to  fetch feedback approximately one day AFTER the deadline.


    4. How can I confirm that I've submitted notebook? 

    Submitted notebooks should be listed under "Submitted assignments"

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    5. How to fetch autograded notebook? Go to Jupyter Hub/Assignments/Submitted assignments. If there is "feedback available to fetch" press Fetch Feedback. After that "view feedback" link should appear. All feedback files are stored in your notebooks directory in a corresponding to each round folder.

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    6. Where can I find solutions for coding tasks?

    In a feedback html file in cells marked as hidden (### BEGIN HIDDEN TEST).

    7. What is the lectures schedule?

    There are no lectures in this course, instead we have exercise session where we discuss both theory and coding exercises.

    Timetable for exercise sessions is at Notebook section and Zoom link is here.

    8. When is deadline for Notebook X., ML Project?

    Find deadlines for notebooks at Notebook section and for ML Project at ML Project section.

    9. Can I get deadline extension?

    Generally, no. Instead you can do bonus tasks, see ML Project section.

    10. Can I get partial points for coding tasks?

    Yes, ask (at channel #grading in Slack or send email) teachers to review your solution. 

    11. How grades are calculated?

    Each jupyter notebook will give you 10 points max. After completing 6 notebooks, you can get 60 points max (30p min to pass). ML project is 40 points max (20p min to pass). Thus, in total you can earn 100 points.

    Grading done as follows:

     points   grade
    50-59 1
    60-69 2
    70-79 3
    80-89 4
    90-100 5

    12. Where can I find my points for coding exercises?

    You can find them in your feedback files AFTER round deadline. At the end of the course points will be imported to MyCourses -> Grades page.

    13. What are the recommended reading material for the course?

    The course book is "Machine Learning: Basic Principles" Alex Jung,  available online.

    Additional material:

    - "Introduction to Machine Learning with Python: A Guide for Data Scientists" Andreas C. Müller ,  Sarah Guido, e-book available from Aalto library.

    - "Python data science handbook" Jake VanderPlas, available online.

    14. I've finished course, how can I get digital badge?

    Please, follow the instructions from FiTech.



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