Yehuda Levy: Catalogue data in Autumn Semester 2017 |
| Name | Mr Yehuda Levy |
| Department | Computer Science |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 263-5200-00L | Data Mining: Learning from Large Data Sets  | 4 credits | 2V + 1U | A. Krause, Y. Levy | |
| Abstract | Many scientific and commercial applications require insights from massive, high-dimensional data sets. This courses introduces principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. | ||||
| Objective | Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, we will study principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course will both cover theoretical foundations and practical applications. | ||||
| Content | Topics covered: - Dealing with large data (Data centers; Map-Reduce/Hadoop; Amazon Mechanical Turk) - Fast nearest neighbor methods (Shingling, locality sensitive hashing) - Online learning (Online optimization and regret minimization, online convex programming, applications to large-scale Support Vector Machines) - Multi-armed bandits (exploration-exploitation tradeoffs, applications to online advertising and relevance feedback) - Active learning (uncertainty sampling, pool-based methods, label complexity) - Dimension reduction (random projections, nonlinear methods) - Data streams (Sketches, coresets, applications to online clustering) - Recommender systems | ||||
| Prerequisites / Notice | Prerequisites: Solid basic knowledge in statistics, algorithms and programming. Background in machine learning is helpful but not required. | ||||