Coursera - Mining Massive Datasets (Stanford University)
WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~77 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 20:04:35 | 2.39 GB
Genre: eLearning Video / Data Science and Big Data
We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.

We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair. When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches. Many other large-scale algorithms are covered as well, as outlined in the course syllabus.

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

General
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Language : English

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