LEARNING OUTCOMES
After the course, the students have an overview of the main principles and methods of data mining and know how to apply them on real world problems. They know the most fundamental pattern types and their search methods, including associative, graph and sequence mining, main approaches to cluster large-dimensional and heterogenous data, and how to validate the data mining results.
Credits: 5
Schedule: 14.09.2021 - 15.12.2021
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Wilhelmiina Hämäläinen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
The course covers fundamental data mining problems, such as pattern discovery, graph mining, and clustering different types of data. The main emphasis is in learning the basic principles of data mining and their application in practice, including method selection, validation, and scalablity issues.
applies in this implementation
Syllabus
Introduction to Data mining
Data preprocessing
Distance and similarity
Clustering
Association mining
Graph mining
Web mining and recommendation systems
Social network analysis
Text mining
Optional topics (like outlier detection, sequential patterns, applications)
Assessment Methods and Criteria
valid for whole curriculum period:
Home assignments, project work, examination.
Workload
valid for whole curriculum period:
Contact teaching 24h lectures + 12h exercises; self studying 90-100h (home assignments, project work, exam preparation).
DETAILS
Study Material
valid for whole curriculum period:
Lecture slides and external material. The course book will be announced later.
applies in this implementation
- Textbook: Charu C. Aggarwal: Data Mining: The Textbook, Springer 2015.
E-book available in Aalto Library. - Lecture slides and possible external material.
- Textbook: Charu C. Aggarwal: Data Mining: The Textbook, Springer 2015.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2020-2021 Autumn I-II
2021-2022 Autumn I-II
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CS-E4650
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
applies in this implementation
Obligatory 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). In addition, some knowledge on linear algebra is highly recommended.
Details on the schedule
applies in this implementation
Lectures on Tuesdays at 16:15-18:00 14.9.-19.10. and 2.11.-7.12. 2021.
Course exam Wed 15.12. 2021. (re-exam 23.2. 2022)
Exercise sessions (not every week) begin on week 38. Participation is optional, but highly recommended.
No teaching on the exam week 25.10.-31.10. 2021.