Difference between revisions of "Class meeting for 10-605 Workflows For Hadoop"
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* Third lecture: Slides [http://www.cs.cmu.edu/~wcohen/10-605/workflows-3.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/workflows-3.pdf in PDF]. | * Third lecture: Slides [http://www.cs.cmu.edu/~wcohen/10-605/workflows-3.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/workflows-3.pdf in PDF]. | ||
| − | * Catchup on simjoins: Slides [http://www.cs.cmu.edu/~wcohen/10-605/simjoins.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/simjoins.pdf in PDF]. | + | * Catchup on simjoins: Slides [http://www.cs.cmu.edu/~wcohen/10-605/simjoins-catchup.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/simjoins-catchup.pdf in PDF]. |
=== Quizzes === | === Quizzes === | ||
Latest revision as of 12:37, 19 September 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.
Slides
- First lecture: Slides in Powerpoint, in PDF.
- Second lecture: Slides in Powerpoint, in PDF.
- Third lecture: Slides in Powerpoint, in PDF.
- Catchup on simjoins: Slides in Powerpoint, in PDF.
Quizzes
Readings
- Pig: none required. A nice on-line resource for PIG is the on-line version of the O'Reilly Book Programming Pig.
- Optional: Introduction to Information Retrieval, by Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütz, has a fairly self-contained chapter on the vector space model, including Rocchio's method.
Also discussed
- Joachims, Thorsten, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proceedings of International Conference on Machine Learning (ICML), 1997.
- Relevance Feedback in Information Retrieval, SMART Retrieval System Experiments in Automatic Document Processing, 1971, Prentice Hall Inc.
- Schapire et al, Boosting and Rocchio applied to text filtering, SIGIR 98.
Things to Remember
- The TFIDF representation for documents.
- What dataflow languages are, what sort of abstract operations they use, and what the complexity of these operations is.
- How joins are implemented in dataflow (and the difference between map-side and reduce-side joins)
- What the PageRank algorithm is
- Common ways of representing graphs in map-reduce system
- A list of edges
- A list of nodes with outlinks
- Why iteration is often expensive in pure dataflow algorithms.
- How Spark differs from and/or is similar to other dataflow algorithms
- Actions/transformations
- RDDs
- Caching
- Definition of a similarity join/soft join.
- Why inverted indices make TFIDF representations useful for similarity joins
- e.g., whether high-IDF words have shorter or longer indices, and more or less impact in a similarity measure
