seminar: robust data mining techniques - pre-course meeting€¦ · example: linear regression 2 0...
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Seminar: Robust Data Mining TechniquesPre-course meeting
Technische Universitat MunchenDepartment of InformaticsData Mining and Analyticskdd.in.tum.de
January 27, 2017
Introduction
Why robustness matters?
• Data is inherently noisy
• Guarantees inperformance-critical areas
• Adversarial scenarios
• Crowdsourcing
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Example: Linear Regression
2 0 2 4 6 8 10 1250
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250Fit without outlierFit with outlier
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Example: Deep Learning
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Topics
Robust extensions to data mining algorithms
• Matrix factorization
• Regression
• Classification
• Clustering
• Time series analysis
Further topics in robust machine learning
• Robustness of complex networks
• Differential privacy
• Fooling learning algorithms with adversarial examples
• Designing algorithms for the adversarial setting
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Learning Outcome
You are going to learn
• about the design of robust data mining algorithms
• to read and understand scientific publications
• to write a scientific report
• how to prepare and give a technical talk
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Requirements
Paper
• 5 - 8 pages
• Latex template on the course webpage
Presentation
• 30 minutes talk
• 15 minutes discussion
Reviews
• Everyone has to review 2 papers by other students
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Deadlines
• 1 week before the talk - submission of extended abstractand slides
• Day of the talk - submission of preliminary paper for review
• 1 week after the talk - receiving comments from reviewers
• 2 weeks after the talk - submission of the final paper
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Grading
The grade is determined based on
• Report
• Presentation (slides and speech)
• Reviews written by you
• Involvement in the class
• Interactions with the supervisor
• Extra bonuses for own contributions (e.g. visualizations,demos, experiments)
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Schedule
• Before 08.02. - fill out the pre-course surveyhttps://goo.gl/forms/rWxXhsMpZPW6kpcE2
• 03.02. - 08.02. - registration via the matching system
• After 15.02. - notification of the participants and selection oftopics
• April - June - weekly sessions every Monday 14:30 - 16:00
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Questions?
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