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

Osion kuvaus

• ML Project
  ML project is a series of small peer-graded assignments, where we will formulate real-life problem as a machine learning problem, by applying ML methods learnt during the course. Python implementation is optional.

  ML project is completed incrementally in three stages (40 points in total, see table below):

  • Stage 1. Machine learning – when and why? (7p max)
  • Stage 2. ML problem formulation – Data (11p max)
  • Stage 3. ML problem formulation – Model and Loss (22p max)

  
  

  Each stage includes a submission of a report and its peer grading. You will have an opportunity to improve project parts based on the feedback from peer-reviews and submit edited version in the next stage. Given time constrains and expected workload (2 credits = 54 hours), we do not ask to do any numerical experiments, but to identify appropriate problem, formulate it as ML problem and plan implementation details.
  

  
  

  Note, these three peer-graded assignments are mandatory!

  
  

  In addition, we will have two bonus tasks:

  • ML problem - Python implementation (points TBA)
  • Cost of ML project (points TBA)

  ML project time table and points:

  
  

  	ML - when & why?	Problem formulation  DATA	Problem formulation  MODEL & LOSS
  Points (submission + peer-grading)	5 + 2	6 + 5	14 + 8
  Submission opens	5 June, 08:00	19 June, 08:00	3 July, 08:00
  Submission closes	12 June, 23:59	26 June, 23:59	10 July, 23:59
  Peer review closes	19 June, 23:59	3 July, 23:59	17 July, 23:59
  Description	Click here	Click here	Click here
  Grading criteria	Click here	Click here	Click here

  
  

  The points achieved during each stage consist of two components:
  

  1. quality of your report, assessed by peer graders (students or course staff)
     
  2. quality of your review (e.g., gradings are well-justified)

  
  

  Before submission check "Grading criteria", these exact criteria are used for peer-grading.

  
  

  To get maximum points you must complete all the peer evaluations that will be assigned to you.

  • Valitse aktiviteetti Bonus task. ML problem – Python implementation

    

    Bonus task. ML problem – Python implementation Tehtävä
  • Valitse aktiviteetti Bonus task. ML project cost.

    

    Bonus task. ML project cost. Tehtävä
  • Valitse aktiviteetti Bonus task. Trustworthiness of ML solution

    

    Bonus task. Trustworthiness of ML solution Tehtävä
  • Valitse aktiviteetti DEMOS. Here you can find an example of ML Project,...

    DEMOS.

    Here you can find an example of ML Project, peer-review demo with corresponding explanations:
    

    • ML Project Demo
      
    • peer-review Demo
      
  • Valitse aktiviteetti Machine Learning - when and why?

    

    Machine Learning - when and why? Työpaja
  • Valitse aktiviteetti Reading material "Machine Learning - when and why?...

    Reading material "Machine Learning - when and why?"

    -mlbook.cs.aalto.fi , Chapter 1 (up to p.19)

    -Lecture notes CS 329S: Machine Learning Systems Design (Stanford, 2022), Lecture 1. Machine Learning Systems in Production, Sections “When to Use Machine Learning”, “Requirements for ML Systems”, "Stakeholders and their objectives".

  • Valitse aktiviteetti ML problem formulation - DATA

    

    ML problem formulation - DATA Työpaja
  • Valitse aktiviteetti Reading material "ML problem formulation - DATA" m...

    Reading material "ML problem formulation - DATA"

    mlbook.cs.aalto.fi , Chapter 2.1

    Lecture notes CS 329S: Machine Learning Systems Design (Stanford, 2022), Lecture 3.Training data, Sections "Sampling", "Class Imbalance", Lecture 10. Machine Learning Systems in Production, Section "Data distribution shift".

  • Valitse aktiviteetti ML problem formulation - MODEL and LOSS

    

    ML problem formulation - MODEL and LOSS Työpaja
  • Valitse aktiviteetti Reading material "ML problem formulation - MODEL a...

    Reading material "ML problem formulation - MODEL and LOSS"

    mlbook.cs.aalto.fi , Chapter 2.2, 2.3 & 6.2.