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ELEC-E7260 - Machine Learning for Mobile and Pervasive systems P, 31.10.2018-15.02.2019

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Syllabus

General

  • General

    General

    This lecture promotes understanding of good practices for machine learning with noisy and inaccurate data.

    In particular, we focus on feature extraction/ feature subset selection, handling high dimensional data, ANN + Deep Learning, Probabilistic graphical models, Topic models; as well as Unsupervised learning and clustering, Anomaly detection and Recommender systems.

    The lecture exercises are project-based and foster hands-on experience with machine learning approaches on relevant data sets.

    The lecture schedule is given below:

    schedule


    Attendance in to the lectures is mandatory. We will assign Machine-learning projects which are solved in teams of 3 students each. The groups will document their project progress in an academically written paper. The results achieved throughout the project are presented in the end of the course in form of a poster.

    In addition, throughout the course, each group will be assigned a tutorial to present during the lecture. Furthermore, the project progress is presented to the other groups twice during the course.

    The grading structure is described below. Based on the success in the respective parts of the course, students score points which then translate into the final grade. 50% of the total points are sufficient to pass the course.

    grading

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  • ELEC-E7260 - Machine Learning for Mobile and Pervasive systems P, 31.10.2018-15.02.2019
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  • Schools
    • School of Arts, Design, and Architecture (ARTS)
    • School of Business (BIZ)
    • School of Chemical Engineering (CHEM)
    • –sGuides for students (CHEM)
    • – Instructions for report writing (CHEM)
    • School of Electrical Engineering (ELEC)
    • School of Engineering (ENG)
    • School of Science (SCI)
    • Language Centre
    • Open University
    • Library
    • Aalto university pedagogical training program
    • UNI (exams)
    • Sandbox
  • CORONAVIRUS INFO
    • Koronavirus - tietoa opiskelijalle
    • Coronavirus - information for students
    • Coronavirus - information för studerande
    • Koronaviruksen vaikutus opiskeluun: kysymyksiä ja vastauksia
    • Effects of the coronavirus on studies: questions and answers
    • Coronaviruset och studierna: frågor och svar
    • Corona help for teachers
  • Service Links
    • MyCourses
    • - Instructions for Teachers
    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Instructions for Students
    • - Workspace for thesis supervision
    • WebOodi
    • Into portal for students
    • Courses.aalto.fi
    • Library Services
    • - Resourcesguides
    • - Imagoa / Open science and images
    • IT Services
    • Campus maps
    • - Search spaces and see opening hours
    • Restaurants in Otaniemi
    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
    • Support for Studying
    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
  •   ‎(en)‎
    •   ‎(en)‎
    •   ‎(fi)‎
    •   ‎(sv)‎