Översikt

  • Allmänt

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

    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
    •        Boosting
    •        Neural networks (MLPs)

     Part III: Additional learning models 

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

    Course position and Prerequisites 

    Course is MSc course in Machine learning, targeted to 1st year MSc students in CCIS and Life Science Technologies programmes. 

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

    • CS-C3190 Machine Learning, or MS-C1620 Statistical inference, or equivalent knowledge
    • Basics of probability theory
    • Basic linear algebra
    • 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

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

    • Lectures (online): Tuesdays 10:15-12:00 (first lecture 14.9.2021), streamed online and recorded (See the tabs Lecture slides and Streaming/Recording). Attending the lectures is voluntary.
    • Assignments : completed at home, and submitted online (See the tab Assignments).
    • Tutorial sessions: Fridays 10:15-12:00. The sessions alternate between
      • Q&A sessions (help for solving the exercises).  We will organize the Question & Answer sessions as chat sessions, where the submitted questions (to the "General discussion" section) will be answered by text during the already specified schedule. Please remember to submit your questions 24 hours ahead of the session and avoid submitting repetitive questions. Attending the Q&A sessions is voluntary. 
      • Solution sessions (presenting the solutions for the exercise set). Attending the solution sessions is voluntary.
    • Exam (online):   20.12.2021 at 17.00–20.00. The exam will be open book.
      • Repeat exam (online): 22.2.2022 at 17:00-20:00. 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, Xiao Haoping, Bruce Nguyen, Mark Seliaev


    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: