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Handouts:

1/5: Introduction

• Introduction [slides] [reading]

1/7: MapReduce

• MapReduce [slides] [reading]

1/12: Recommendation System

• Recommendation System [slides] [reading]

1/14: Near Neighbor Search in High Dimensional Data

• Near Neighbor Search in High Dimensional Data [slides] [reading]

1/19: Locality Sensitive Hashing (LSH)

• Locality Sensitive Hashing (LSH) [slides] [reading]

1/21: Structure of the webgraph, PageRank and Project ideas

• Project ideas and available datasets [slides]
• Structure of the webgraph and PageRank [slides]

1/22: Section on Map-Reduce infrastructure

• Section1 [slides1] [slides2]
  [Aster Examples used in the section]
  [Links to ant and ncluster_loader script]
  [AWS & Hive]

1/26: Link Analysis

• Structure of the webgraph and PageRank [slides][reading]

1/28: HITS and web spam

• Personalized PageRank, Hubs and Authorities, TrustRank [slides][reading]
• Readings:
  • Authoritative sources in a hyperlinked environment by J. Kleinberg, JACM 1999.

2/2: Web spam

• Web spam and TrustRank [slides][reading]
• Readings:
  • Combating Web Spam with TrustRank by Z. Gyongyi, H. Garcia-Molina, J. Pedersen, VLDB 2004.

2/4: Proximity on Graphs

• Random Walks with Restarts and Center piece subgraphs [slides]
• Readings:
  • Center-Piece Subgraphs: Problem Definition and Fast Solutions by H. Tong, C. Faloutsos, KDD 2006.
  • Fast Random Walk with Restart and Its Application by H. Tong, C. Faloutsos, J.Y. Pan, ICDM 2006.

2/9: Dimensionality reduction

• SVD and CUR [slides]
• Readings:
  • Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition by P Drineas, R Kannan, MW Mahoney, SIAM Journal of Computing 2007.
  • Tensor-CUR Decompositions For Tensor-Based Data by M. W. Mahoney, M. Maggioni, and P. Drineas, KDD 2003.
  • Less is More: Compact Matrix Decomposition for Large Sparse Graphs by J. Sun, Y. Xie, H. Zhang, C. Faloutsos, SDM 2007.

2/11: Clustering

• Clustering [slides][reading]

2/16: Mining data streams

• Data Streams [slides][reading]

2/18: Mining data streams (Cont)

• Data Streams [slides][reading]

2/23: Large scale supervised machine learning (1)

• k-nearest neighbor, Perceptron[slides]
• Readings:
  • Learning Using Large Datasets by L. Bottou, O. Bousquet, MMSD 2009.
  • Map-Reduce for Machine Learning on Multicore, C. Chu, S. Kim, Y. Lin, Y. Yu, G. Bradski, A. Ng, K. Olukotun, NIPS 2006.

2/25: Large scale supervised machine learning (2) (guest lecture by Sugato Basu)

• Classification and regression trees [slides]
• Readings:
  • PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce by B. Panda, J. Herbach, S. Basu, R. Bayardo, PVLDB 2009.

3/2: Large scale supervised machine learning (3)

• Support Vector Machines, Cutting plane algorithm, SVM for structured output prediction [slides]
• Readings:
  • Pegasos: Primal Estimated sub-GrAdient SOlver for SVM by S. Shalev-Shwartz, Y. Singer, N. Srebro, ICML 2007.
  • A Suport Vector Method for Optimizing Average Precision by Y. Yue, T. Finley, F. Radlinski, T. Joachims, SIGIR 2007.
  • Learning Using Large Datasets by L. Bottou, O. Bousquet, MMSD 2009.

3/4: Association Rules

• Frequent itemsets and Association rules [slides][reading]

3/9: Optimizing submodular functions

• Submodular functions, outbreak detection in networks, finding influencers in networks [slides]
• Readings:
  • Near-optimal Nonmyopic Value of Information in Graphical Models by A. Krause, C. Guestrin. UAI, 2005.
  • Maximizing the Spread of Influence through a Social Network by D. Kempe, J. Kleinberg, E. Tardos, KDD 2003.
  • Cost-effective Outbreak Detection in Networks by J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, N. Glance, KDD 2007.

3/11: Mining the Web for Structured Data

• Mining the Web for Structured Data [slides]