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Handouts

Homeworks

All problem sets should include the cover sheet (PDF, LATEX). We will take off 2 points for failing to include the cover sheet.

Lecture notes

• 01/08: Introduction
  Slides: Introduction and MapReduce
  Reading: Ch1: Data Mining and Ch2: Large-Scale File Systems and Map-Reduce
• 01/10: Frequent itemsets and Association rules
  Slides: Association Rules
  Reading: Ch6: Frequent itemsets
• 01/15: Locality Sensitive Hashing
  Slides: Finding Similar Items: Locality Sensitive Hashing
  Reading: Ch3: Finding Similar Items
• 01/17: Theory of Locality Sensitive Hashing
  Slides: Theory of Locality Sensitive Hashing
  Reading: Ch3: Finding Similar Items
• 01/22: Clustering
  Slides: Clustering
  Reading: Ch7: Clustering
• 01/24: Dimensionality Reduction: SVD and CUR
  Slides: Dimensionality Reduction: SVD and CUR
  Reading: Ch11: Dimensionality Reduction
• 01/29: Recommender Systems 1
  Slides: Recommender systems: Content-based and Collaborative filtering
  Reading: Ch9: Recommendation systems and the The Long Tail in Wired.
• 01/31: Recommender Systems 2
  Slides: Recommender systems: Latent Factor Models
  Reading: Ch9: Recommendation systems
• 02/05: Link Analysis: PageRank
  Slides: PageRank
  Reading: Ch5: Link Analysis
• 02/07: Link Analysis: Web spam and TrustRank, Random Walks with Restarts
  Slides: TrustRank and Web spam
  Reading: Ch5: Link Analysis
• 02/12: Analysis of Massive Graphs
  Slides: Discovering Clusters in Networks
  Reading: Ch10: Analysis of Social Networks
• 02/14: Large-Scale Machine Learning: k-Nearest Neighbors, Perceptron
  Slides: Trawling and Perceptron
  Reading: Ch12: Large-Scale Machine Learning
• 02/19: Large-Scale Machine Learning: Support Vector Machines
  Slides: Support Vector Machines
  Reading: Ch12: Large-Scale Machine Learning
• 02/21: Large-Scale Machine Learning: Decision Trees
  Slides: Decision Trees on MapReduce
  Reading: PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce by Panda, Herbach, Basu, Bayardo. VLDB 2009.
• 02/16: Mining Data Streams 1
  Slides: Mining Data Streams (Part 1)
  Reading: Ch4: Mining data streams
• 02/28: Mining Data Streams 2
  Slides: Mining Data Streams (Part 2)
  Reading: Ch4: Mining data streams
• 03/05: Web Advertising
  Slides: Advertising on the Web
  Reading: Ch8: Advertising on the Web
• 03/07: Learning through Experimentation
  Slides: Multiarmed bandits
  Reading: A Contextual-Bandit Approach to Personalized News Article Recommendation by Li, Chu, Langford, Schapier. WWW 2010.
• 03/12: Optimizing Submodular Functions
  Slides: Submodular functions
  Reading: Turning Down the Noise in the Blogosphere by El-Arini, Veda, Shahaf, Guestrin. KDD 2009.
  Cost-effective Outbreak Detection in Networks by Leskovec, Krause, Guestrin, Faloutsos, VanBriesen, Glance. KDD 2007.
• 03/14: Review
  Slides: Review

All readings have been derived from the Mining Massive Datasets by Anand Rajaraman and Jeff Ullman.

Sections

• 01/13: Probability Theory Rewiew session
  Slides: [pdf]
• 01/18: Linear Algebra Review Session
  Slides: [pdf]