Learning outcomes of the course unit
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Course Description
This course will cover a number of advanced topics in data mining. A mix of lectures and readings will familiarize the students with recent methods and algorithms for exploring and analyzing large-scale data and networks, as well as applications in various domains (e.g., web science, social science, neuroscience). The focus will be on scalable and practical methods, and the students will have the chance to analyze large datasets. The advanced topics will include: ranking, classification, clustering and community detection, summarization, similarity, anomaly detection, node representation and deep learning in the graph setting.
Learning Outcomes of the Course:
This course aims to introduce students to advanced data mining, with emphasis on interconnected data or graphs or networks. Students will become familiar with the challenges of processing large amounts of data, state-of-the-art methods and algorithms for analyzing them, and applications of data mining in various domains. By the end of the course, students will have a thorough understanding of the graph mining foundations.
After completing the course, students should be able to :
•will have a thorough understanding of the graph mining foundations
•critique data mining methods,
•formulate and solve new problems, and
•analyze large-scale datasets (in distributed and other settings).
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Recommended or required reading
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Machine Learning, 2nd edition, by Ethem Alpaydin
Course reading is composed of articles, laws as well as book chapters. Additional information will be distributed either electronically or delivered in printed forms.
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