Topic outline

  • General

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    Course logistics


    Schedule


    Date (dd.mm.yyyy)   Activity Location
    02.03.2022 (12:15) Lecture 1 Zoom
    09.03.2022 (12:15) Lecture 2 Zoom
    10.03.2022 (16:15) Python Tutorial TU1 Saab Auditorium
    11.03.2022 Assignment 1 release
    16.03.2022 (12:15) Lecture 3
    17.03.2022 (16:15) Assignment 1 exercise session   TU1 Saab Auditorium
    21.03.2022 (16:00) Assignment 1 due
    23.03.2022 (12:15) Lecture 4 Zoom
    25.03.2022 Assignment 2 release
    30.03.2022 (12:15) Lecture 5 Zoom
    31.03.2022 (16:15) Assignment 2 exercise session   TU1 Saab Auditorium
    06.04.2022 (12:15) Lecture 6 Zoom
    06.04.2022 (16:00) Assignment 2 due
    20.04.2022 (12:15) Lecture 7 Zoom
    22.04.2022 Assignment 3 release
    27.04.2022 (12:15) Lecture 8 Zoom
    28.04.2022 (16:15) Assignment 3 exercise session   TU1 Saab Auditorium
    04.05.2022 (12:15) Lecture 9 (by Dr. Riikka Huusari)  Zoom
    04.05.2022 (16:00) Assignment 3 due
    11.05.2022 (12:15) Self study of review slides
         (No zoom lecture)
    02.06.2022 (09:00) Exam Check Sisu for location

    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 personnel

    • Lecturer: Rohit Babbar
    • Course assistants: Petrus Mikkola, Adrian Müller, Mohammadreza Qaraei

    Grading

    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.

    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 (We will use Python, but there will be an introductory python tutorial.)

    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.

    Language of Instruction: English

    Course Material

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

    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.