Topic outline

  • Assignments

    The course includes nine assignments A1, A2, ..., A9. Each assignment consists of small coding tasks that require you to apply ML methods (provided by Python libraries such as "scikit-learn") to different datasets (such as weather data).

    The assignments are in the form of Jupyter notebooks at http://jupyter.cs.aalto.fi, where they are also to be submitted. After logging in with your Aalto credentials, choose the CS-C3240 Machine Learning (2022) server. When it is started, you will see your own personal files.

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      Assignment instructions Page


    •   Deadline     Course Book1 Additional Material
      A1- Data,
      Model, Loss
      		 21.01. 20:00 Ch. 2 Loading Data Demo
      A2 - Regression 28.01. 20:00 Sec. 3.1, 3.2, 3.3. Regression Demo
      A3 - Classification 04.02. 20:00 Sec. 3.6, 3.7
      		Classification Demo
      A4 - Model Validation and Selection 11.02. 20:00 Sec. 6.1 - 6.3
      		 
      A5 - Diagnosing ML 18.02. 20:00 Sec. 6.6.
      		 
      A6 - Regularization 04.03. 20:00  Sec. 7.1 - 7.3
      A7 - Deep Learning 08.04. 20:00 Sec.  3.11.  
      A8 - Clustering 08.04. 20:00 Ch. 8   
      A9 - Feature Learning 08.04. 20:00 Ch. 9   


      1) A. Jung, "Machine Learning. The Basics", Springer, Singapore, 2022, draft: http://mlbook.cs.aalto.fi

      For a quick intro to Python and how to use numpy arrays, we refer to the excellent material at https://aaltoscicomp.github.io/python-for-scicomp/python/



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