Slides

To be added incrementally, usually minutes before the lecture.
Also usually revised after the lecture for typos etc.

• Lecture 1: The data stream model.Counting. Probability tools
• Lecture 2. Frequency problems
• Lecture 3. Finding frequent elements. The CM-sketch and applications
• Lecture 4. Distributed sketching. Linear algebra
• Lecture 5. Graph streams
• Lecture 6. Time change in data streams
• Lecture 7. Data Stream Mining. Building decision trees
• Lecture 8. Evaluation. More predictors. Clustering
• Lecture 9. Frequent pattern mining in data streams