Fundamentals of Machine Learning

Posted on Tue 13 April 2021 in courses

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This course, divided in two trimesters (modules M06 and M08), present fundamental tools used in Machine Learning and Pattern Recognition ranging from the most basic to more advanced (e.g. Logistic Regression, Principal Component Analysis, Linear Discriminant Analysis, Multi-Layer Perceptrons, Gaussian Mixture Models, and Super Vector Machines). This course serves as a pre-requisite for Deep Learning and other master specialisations. It is given to master students at Idiap's Master in AI.


  • Credits: 4 ECTS (equivalent to 100 to 120 working hours)

  • Grading: Lab assignments (50%) and Final exam (50%)

  • Days: Tuesdays, from 9:00 to 10:30

  • Required prior knowledge: Linear algebra, Probabilities and Statistics, Python Programming

  • Syllabus:

    • Linear Regression (week 1 and 2)
    • Logistic Regression (week 3 and 4)
    • Decision Trees (week 5 and 6)
    • Boosting (week 7 and 8)
    • Multi-layer perceptron (week 9 and 10)
    • Final Exam (week 11)
  • Structure:

    Each topic is approached in two activities, where the first one covers theoretical content about one particular topic and the second one discusses practical aspects, as well as the assignments of the corresponding topic.

    Between each theoretical and practical activities a mandatory assignment is provided. Such assignment contains practical exercises (to be implemented in Python) about the corresponding theoretical class. The student has five days in general to deliver such assignment but the specific deadlines are provided for each practical exercise hereafter.