The “Machine Learning for Data Streams with Practical Examples in MOA” textbook is a resource intended to help students and practitioners enter the field of machine learning and data mining for data streams. The online version of the book is now complete and will remain available online for free.
This textbook can now be ordered on Amazon.
Citing the book
To cite this book, please use this bibtex entry:
@book{MOABook2018, title={Machine Learning for Data Streams with Practical Examples in MOA}, author={Bifet, Albert and Gavald\`a, Ricard and Holmes, Geoff and Pfahringer, Bernhard}, publisher={MIT Press}, note={\url{https://moa.cms.waikato.ac.nz/book/}}, year={2018} }
Slides

 Slides used in courses at Telecom ParisTech by Albert Bifet:
 Lecture 1: Introduction
 Lecture 2: Stream and Sketches
 Lecture 3: Dealing with Change
 Lecture 4: Classification
 Lecture 5: Ensemble Methods
 Lecture 6: Regression
 Lecture 7: Clustering
 Lecture 8: Frequent Pattern Mining
 Slides used in courses at Universitat Politecnica de Catalunya by Ricard Gavaldà:
 Lecture 1: The data stream model. Counting. Probability tools
 Lecture 2. Frequency problems
 Lecture 3. Sampling. Finding frequent elements. The CMsketch
 Lecture 4. Distributed sketching – Graphs Streams.
 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)
 Slides used in courses at Telecom ParisTech by Albert Bifet: