Course - Mining of Massive Datasets - DT8116
Mining of Massive Datasets
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About the course
Course content
The course will discuss data mining algorithms for analyzing very large amounts of data. Vital challenges to be covered include similarity search, mining streaming data, and social network analysis.
Learning outcome
Knowledge: Introduction to problems, principles, mechanisms, and techniques connected to mining large datasets. Skills: Similarity search, mining streaming data, social network analysis, synopses for massive data. Competence: Mining massive datasets.
Learning methods and activities
Joint colloquium and self-study. Individual research project related to the topics studied in the course. If the course is taken by a high number of students, the oral examination may be replaced by a written examination.
Further on evaluation
Evaluation form:
A: Report (either a short report on an own research project, or a longer review paper on the state of the art in a selected area).
B: A final oral exam.
The final grade is pass or fail. To pass the course, both the report and the exam must be passed.
Recommended previous knowledge
The course is primarily, but not exclusively, meant for PhD students with a Master’s degree in Computer Science. The students should have successfully taken courses on Algorithms and Database Systems.
Course materials
To be given at the start of the semester. The core of the curriculum will be selected chapters from the book Mining Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman.
Subject areas
- Computer and Information Science