Slides

• Lecture 1: The data stream model. Counting. Probability tools
• Lecture 2. Frequency problems
• Lecture 3. Sampling. Finding frequent elements. The CM-sketch
• Lecture 4. Distributed sketching. Graph streams. (In the 2014 edition, I talked more about graphs; slides here)
• Lecture 5. Linear algebra, dimensionality reduction
• Lecture 6. Managing 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 (see also Albert Bifet's slides)
• Lecture 10. Cancelled. But here you have some slides by other people on distributed stream mining (p.65 onwards) and on multistream mining.