CS-EJ3211 - Machine Learning with Python D, Lectures, 29.5.2023-17.7.2023

Topic outline

• Notebooks
  You can find the Python notebooks containing the coding exercises at https://jupyter.cs.aalto.fi/. Choose server option "CS-EJ3211 Machine Learning with Python (2023)".
  
  Notebook "0_Intro.ipynb" contains an introductory notebook that is not graded. Its purpose is to get you familiarized with basic Python programming and using the Python notebooks for the coding exercises in Notebooks 1 - 6:
  
  

  • Notebook  1 - "Components of Machine Learning"
  • Notebook  2 - "Regression"
  • Notebook  3 - "Model Validation and Selection"
  • Notebook  4 - "Classification"
  • Notebook  5 - "Clustering"
  • Notebook  6 - "Dimensionality Reduction"
  
  

  Notebooks are released sequentially, at the beginning of each week (Monday 8 am).
  
  

  Submit notebooks  at https://jupyter.cs.aalto.fi/

  How to use Jupyter Hub pdf

  • Bonus task

    If you need extra points for notebooks part (30p min to pass), you can do bonus task (15p max), which is released at jupyter hub. The notebook name is `7_bonus_notebook.ipynb`.

    Submit a notebook by 24.07.2023 23:59 at jupyter hub aalto.

  • Timetable

    
    ES - Exercise session (Zoom link).
    DL - Deadline.
    ML project S1 - ML project Stage 1.
    

    	Mon	Wed
    Week 22 (29.5 - 4.6)	Notebook 1 release	ES 14.00-15.00 slides, recording
    Week 23 (5 - 11.6)	Notebook 1 DL 23.59	ES 14.00-15.00 slides, recording
    Week 24 (12 - 18.6)	Notebook 2 DL 23.59  ML project S1 submission DL	ES 14.00-15.00 slides, recording
    Week 25 (19 - 25.6)	Notebook 3 DL 23.59  ML project S1 peer-review DL	ES 14.00-15.00 slides, recording
    Week 26 (26 - 2.7)	Notebook 4 DL 23.59  ML project S2 submission DL	ES 14.00-15.00 slides, recording
    Week 27 (3 - 9.7)	Notebook 5 DL 23.59  ML project S2 peer-review DL	ES 14.00-15.00 slides, recording
    Week 28 (10 - 16.7)	Notebook 6 DL 23.59  ML project S3 submission DL
    Week 29 (17 - 23.7)	ML project S3 peer-review DL

  • Feedback

    Below you can find anonymous surveys for each topic. Leave your feedback for notebook, exercise session and course book here.  We will try to read and respond to the feedback during the course.

  • 

    Feedback "Components of Machine Learning" Questionnaire

    Student must submit this questionnaire to complete it
  • 

    Feedback "Regression" Questionnaire

    Student must submit this questionnaire to complete it
  • 

    Feedback "Model selection and validation" Questionnaire

    Student must submit this questionnaire to complete it
  • 

    Feedback "Classification" Questionnaire

    Student must submit this questionnaire to complete it
  • 

    Feedback "Clustering" Questionnaire

    Student must submit this questionnaire to complete it
  • 

    Feedback "Feature Learning" Questionnaire

    Student must submit this questionnaire to complete it