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    Contents 

    Part I: Theory 

    •         Introduction
    •         Generalization error analysis & PAC learning
    •         Rademacher Complexity & VC dimension
    •         Model selection

    Part II: Algorithms and models 

    • Linear models: perceptron, logistic regession
    • Support vector machines
    • Kernel methods
    • Neural networks (MLPs)
    • Ensemble methods

     Part III: Additional topics

    •        Feature learning, selection and sparsity
    •        Multi-class classification
    •        Preference learning, ranking

    Course position and Prerequisites 

    Course is MSc course in Machine learning, targeted to MSc students in CCIS and Life Science Technologies programmes. The course is also suitable for PhD studies.

    The course assumes basic background in computer science and statistics, as follows:

    • CS-C3240 Machine Learning, or MS-C1620 Statistical inference, or equivalent knowledge
    • Basics of probability theory
    • Basic of linear algebra
    • Basics of multivariate calculus
    • Programming skills (Python preferable)

    Learning Outcomes 

    After the course, the student knows how to recognize and formalize supervised machine learning problems, how to implement basic optimization algorithms for supervised learning problems, how to evaluate the performance supervised machine learning models, and has understanding of the statistical and computational limits of supervised machine learning, as well as the principles behind commonly used machine learning models.

    Course schedules

    • Lectures (A-hall/Aalto-hall - Y202a, Undergraduate Centre): Tuesdays 10:15-12:00 (first lecture 6.9.2022). Attending the lectures is voluntary. The lectures are recorded and slides are published in MyCourses before each lecture (See the tabs Lecture slides and Recordings). 
    • Assignments : completed at home, and submitted online (See the tab Assignments).
    • Tutorial sessions (A-hall/Aalto-hall - Y202a, Undergraduate Centre): Every other Friday 10:15-12:00 (first session 16.9.2022).  TAs present the model solutions for the exercises. Attending the solution sessions is voluntary.
    • Exam (online in MyCourses):   12.12.2022 at 17.00–20.00. The exam will be open book.
      • Repeat exam (online in MyCourses): 21.2.2023 at 09.00–12.00. The exam will be open book. The same grading principles will be used for the repeat exam as for the original exam.

    Course personnel

    • Lecturer: Prof. Juho Rousu
    • Course assistants: Dr Sandor Szedmak,  Dr Riikka Huusari, Arina Odnoblyudova, Atreya Ray, Hau Phan 


    Grading 

    The course can be completed by two alternative ways:

    • Exercises (max 30 points) + Exam (max 70 points) , giving a grade 0..5. Lowest passing points total is 50. 85 points will give the grade of 5.
    • Exam only (max. 100 points), giving a grade 0...5. 50 points will give the grade 1, 85 points will give the grade of 5.

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

    Language of Instruction 

    English

    Course Material 

    Lecture slides and exercises are the examined content

    Additional reading

    The lectures are mostly based on the books:


    Discussion forum

    MyCourses discussion forum will be available when the course begins available, if you have questions. Teaching assistant monitor the forum and aim to answer within 24 hours.