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Mining Massive Datasets

Course Summary

This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.


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    Course Syllabus

    Week 1:
    MapReduce
    Link Analysis -- PageRank Week 2:
    Locality-Sensitive Hashing -- Basics + Applications
    Distance Measures
    Nearest Neighbors
    Frequent Itemsets Week 3:
    Data Stream Mining
    Analysis of Large Graphs Week 4:
    Recommender Systems
    Dimensionality Reduction Week 5:
    Clustering
    Computational Advertising Week 6:
    Support-Vector Machines
    Decision Trees
    MapReduce Algorithms Week 7:
    More About Link Analysis --  Topic-specific PageRank, Link Spam.
    More About Locality-Sensitive Hashing

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    Recommended Background

    A course in database systems programming (e.g., SQL) is recommended, as is a basic course on algorithms and data structures.

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    Course Format

    There will be about 2 hours of video to watch each week, broken into small segments.  There will be automated homeworks to do for each week, and a final exam.

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    Suggested Reading

    There is a free book "Mining of Massive Datasets, by Leskovec, Rajaraman, and Ullman (who by coincidence are the instructors for this course :-).  You can download it at http://www.mmds.org/  Hardcopies can be purchased from Cambridge Univ. Press.


Course Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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