Prof. Faye Briggs, DH 2062

Keck 107

Fri 11:00 am - 11:50 am

Introduction to the course

Course outline and student presentation assignment

Big Data: Applications & Platform Architectures

Big Data and Analytics Systems: Computer System Architecture

Distributed file systems and map-reduce as a tool for creating parallel algorithms that succeed on very large amounts of data

2/613/2015

Similarity search, including the key techniques of min-hashing and locality sensitive hashing

Data-stream processing and specialized algorithms for dealing with data that arrives so fast it must be processed immediately or lost

Anomaly Detection Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements

The technology of search engines, including Google’s Page Rank, link-spam detection, and the hubs-and-authorities approach

Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements

Algorithms for clustering very large, high-dimensional datasets

Two key problems for Web applications: managing advertising and recommendation systems

Algorithms for analyzing and mining the structure of very large graphs, especially social-network graphs

Techniques for obtaining the important properties of a large dataset by dimensionality reduction, including singular-value decomposition

and latent semantic indexing

Machine-learning algorithms that can be applied to very large data, such as perceptrons, support-vector machines, and gradient descent