Topic outline






  • Note: All sessions of the course are online, there are no physical lectures or exercise sessions!

    Contents 

    Inner product spaces, Kernels, Reproducing kernels, and RKHS. Introductory learning theory and Generalization. Empirical Risk Minimization, Uniform Convergence and Rademacher Complexity. Kernel Ridge Regression and Logistic Regression. Optimization and Duality. Margin-based methods and Support vector machines. Unsupervised learning including clustering, PCA and their kernel variants. 

    Course position and Prerequisites 

    Course is MSc level in Machine learning, targeted to 1st/2nd year MSc students in Machine Learning and Computer science.  Also suitable for PhD studies.

    Prerequisite knowledge:

    • The course Machine Learning: Supervised Methods (CS-E4710), or equivalent knowledge, strongly recommended
    • Basics of probability and linear algebra
    • Python programming skills are recommended (course material will use Python, and numpy. There will be an introductory python programming session)

    Learning Outcomes 

    After attending the course, the student knows the basics of kernels,  positive-definiteness, and RKHS. The courses also formally introduces the notions of generalization in machine learning by studying the principle of Empirical Risk Minimization and its consistency. The student knows how convex optimization methods can be used to efficiently train kernel-based and large-scale linear models.It is also discussed how to apply kernel based methods for unsupervised learning such as PCA


    Course schedule

    Note: All sessions of the course are online, there are no physical lectures or exercise sessions.

    • Lectures (online): Pre-recorded lectures will be made available before the lectures scheduled (every Wednesday afternoon).
    • Assignments : completed at home, and submitted online.
    • Tutorial sessions: Details will posted shortly. Attending the Q&A sessions is voluntary. 
      • Exam (online): The exam will be open book.

      Date (dd.mm.yyyy)
            Activity Location
                03.03.2021      Lecture 1
       pre-recorded
                10.03.2021      Lecture 2
       pre-recorded
                11.03.2021
       16:15 - 18:00 (EEST)
       Python Tutorial
        Zoom session
                17.03.2021
         Lecture 3
       pre-recorded
                24.03.2021
          Lecture 4
        pre-recorded
                 25.03.2021
      16:00 - 18:00 (EEST)
         Assignement 1
         Tutorial Session
        Zoom session
               31.03.2021   Lecture 5   pre-recorded
               07.04.2021   Lecture 6  pre-recorded
               08.04.2021
      16:00 - 16:45 (EEST)
          Q&A session
      for Lecture 5 & 6
          Zoom
               21.04.2021   Lecture 7   pre-recorded
               22.04.2021  Tutorial session
        for assignment 2
         Zoom
              28.04.2021   Lecture 8   pre-recorded
             29.04.2021
           ( 16:00 - 16:40)
           Q&A session
        for lectures 7 and 8
        Zoom
             05.05.2021
           (12:15 - 14:00)
          Lecture 9 by 
         Riikka Huusari
        Live lecture: https://aalto.zoom.us/j/66519899527
      (Recording will be available online afterwards)
            12.05.2021    Lecture 10   Lecture 10 slides - course review for self study


      The course can be completed by two alternative ways:

      • Assignments (max. 50 points) + Exam  (max. 50 points) giving a grade 0..5. The  assignment part has a weightage of 50% and exam of 50%. In aggregate, lowest passing points total is 40. 85 points will give the grade of 5.
      • Exam only (max. 50 points), giving a grade 0...5. 20 points will give the grade 1, 45 points will give the grade of 5.

      The better of the resulting two grades will be taken into account.



      Course personnel

      • Lecturer: Rohit Babbar
      • Course assistants: Petrus Mikkola, Tejas Kulkarni, Mohammadreza Qaraei

      Language of Instruction - English

      Course Material 

      • Lecture slides and exercises are the examined content
      • Further references will be provided during respective lectures 







































      Additional reading

        • Research papers provided during the course (See Materials).