CS-C3240 - Machine Learning D, Lecture, 10.1.2022-8.4.2022

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.

  
  

  • Select activity Assignment instructions

    

    Assignment instructions Page
  • Select activity Deadline Course Book1 Additional Material A1- Data...

    
    

    	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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