Topic outline

  • General

    Note: The course is given remotely this autumn 2020. The lectures and exercise sessions are given by zoom and the zoom links will be added here (under sections Lectures and Exercises) before each teaching session begins. The exam will also be taken remotely.

    Overview

    The course gives an overview of the main principles and methods of data mining and how to apply them on real world problems. It introduces the most fundamental pattern types and their search methods, including associative and graph patterns, main approaches to clustering large-dimensional and/or heterogeneous data, web and text mining, social community detection and validation of data mining results.

    Prerequisites

    Programming skills (CS-A1110 or equivalent), data structures and algorithms (CS-A1140 or equivalent), basic concepts of probability and statistics (MS-A050* or equivalent).

    Material

    The course is based on textbook Charu C. Aggarwal: Data mining - the textbook. Springer 2015. The e-book available in Aalto library. Lectures notes, links to video recordings and other material will be added here in MyCourses.

    Workload

    During period 1, the workload consists of about 36h contact sessions (or equivalent self-studying) and 30h home assignments, and during period 2, about 50h project work and 10-20h preparation for the exam.

    Grading

    Course performance consists of three elements:
    1. three graded home assignments (period 1)
    2. project work (period 2)
    3. final exam (period 2)
    The course grade is based on a sum points in all categories (30%+35%+35%=100%). To pass the course you should get 50% of total points and at least 25% of max points in each category. In the grading of assignments, we will use "I-don't-know policy", which means that "I don't know'' answers receive 15% of the grade.



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