Course Information (Spring 2015)
Instructor: | Prof. Faye Briggs, DH 2062 | Graduate TAs: | Deepak Majeti, DH 2069 |
|---|---|---|---|
Lectures: | Keck 107 | Lecture times: | F 11:00 am - 11:50am |
Schedule for Student Presentations
Note: The slides and related material for each topic will be provided. Hence, do not hesitate about the workload if you like a topic not in your domain.
Topic | Student Name | Date | Presentation | |
|---|---|---|---|---|
| 1 | Distributed file systems and map-reduce as a tool for creating parallel algorithms that succeed on very large amounts of data | | 2/6/2015 | |
| 2 | Similarity search, including the key techniques of min-hashing and locality sensitive hashing | 2/13/2015 | ||
| 3 | Data-stream processing and specialized algorithms for dealing with data that arrives so fast it must be processed immediately or lost | 2/20/2015 | ||
| 4 | The technology of search engines, including Google’s Page Rank, link-spam detection, and the hubs-and-authorities approach | 2/27/2015 | ||
| 5 | Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements | 2/6/2015 | ||
| 6 | Algorithms for clustering very large, high-dimensional datasets | 3/13/2015 | ||
| 7 | Two key problems for Web applications: managing advertising and recommendation systems | 3/20/2015 | ||
| 8 | Algorithms for analyzing and mining the structure of very large graphs, especially social-network graphs | 3/27/2015 | ||
| 9 | Techniques for obtaining the important properties of a large dataset by dimensionality reduction, including singular-value decomposition and latent semantic indexing | 4/3/2015 | ||
| 10 | Machine-learning algorithms that can be applied to very large data, such as perceptrons, support-vector machines, and gradient descent | 4/10/2015 |
Resources
Text Book: Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
Systems Architecture for Big Data and Analytics: A Big Data Architecture for Large Scale Security Monitoring, by Samuel Marchal, Xiuyan Jiang, Radu State, Thomas Engel
Databases & Tools: Hadoop & HDFS, Hive, SPARK, Map-Reduce Google Big Table & GoogleFS, Google Cluster Experiences with MapReduce
Programming Approaches to Big Data Analytics: OpenMP, MPI, etc
Analytics Algorithms and Applications:
GraphX: Unifying Table and Graph Analytics, Joseph Gonzalez
Analytics for all: Challenges in analytics applications
Machine Learning Review: Machine Learning Foundation, By Jason Brownlee
Modeling and Detection Techniques for Counter-Terror Social Network Analysis and Intent Recognition, by Clifford Weinstein, William Campbell, Brian Delaney, Gerald O’Leary
Visualization Tools:
Cell Phone Mini Challenge Award: Intuitive Social Network Graphs Visual Analytics of Cell Phone Data using MobiVis and OntoVis, by Carlos D. Correa Tarik Crnovrsanin Christopher Muelder Zeqian Shen Ryan Armstrong James Shearer Kwan-Liu Ma ∗