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Handouts

Sample Final Exams

• 2011 final exam with solutions
• 2013 final exam with solutions

Assignments

Gradiance (no late periods allowed):

• GHW 1: Due on 1/14 at 11:59pm.
• GHW 2: Due on 1/21 at 11:59pm.
• GHW 3: Due on 1/28 at 11:59pm.
• GHW 4: Due on 2/04 at 11:59pm.
• GHW 5: Due on 2/11 at 11:59pm.
• GHW 6: Due on 2/18 at 11:59pm.
• GHW 7: Due on 2/25 at 11:59pm.
• GHW 8: Due on 3/03 at 11:59pm.
• GHW 9: Due on 3/10 at 11:59pm.

Homeworks (1 late period allowed):

• HW0 (Hadoop tutorial) to help you set up Hadoop: Due on 1/12 at 11:59pm. Solutions: [PDF][Code].
• HW1: Due on 1/21 at 11:59pm. Submission Templates: [pdf | tex | docx] Solutions: [PDF][Code].
• HW2: Due on 2/04 at 11:59pm. Submission Templates: [pdf | tex | docx] Solutions: [PDF][Code].
• HW3: Due on 2/18 at 11:59pm. Submission Templates: [pdf | tex | docx] Solutions: [PDF][Code].
• HW4: Due on 3/03 at 11:59pm. Submission Templates: [pdf | tex | docx] Solutions: [PDF][Code].

Lecture notes

• 01/05: Introduction; MapReduce
  Slides: [pptx], [pdf]
  Reading: Ch1: Data Mining (Sect. 2.1-2.4) and Ch2: Large-Scale File Systems and Map-Reduce
• 01/07: Frequent Itemsets Mining
  Slides: [pptx], [pdf]
  Reading: Ch6: Frequent itemsets
• 01/12: Locality-Sensitive Hashing I
  Slides: [pptx], [pdf]
  Reading: Ch3: Finding Similar Items (Sect. 3.1-3.4)
• 01/14: Locality-Sensitive Hashing II
  Slides: [pptx], [pdf]
  Reading: Ch3: Finding Similar Items (Sect. 3.5-3.8)
• 01/19: Clustering
  Slides: [pptx], [pdf]
  Reading: Ch7: Clustering (Sect. 7.1-7.4)
• 01/21: Dimensionality Reduction
  Slides: [pptx], [pdf]
  Reading: Ch11: Dimensionality Reduction (Sect. 9.4)
• 01/26: Recommender Systems I
  Slides: [pdf]
  Reading: Ch9: Recommendation systems
• 01/28: Recommender Systems II
  Slides: [pdf]
  Reading: Ch9: Recommendation systems
• 02/02: Link Analysis I: PageRank
  Slides: [pptx], [pdf]
  Reading: Ch5: Link Analysis (Sect. 5.1-5.2)
• 02/04: Link Analysis II: Link Spam, HITS
  Slides: [pptx], [pdf]
  Reading: Ch5: Link Analysis (Sect. 5.3-5.5)
• 02/09: Analysis of Massive Graphs I
  Slides: [pdf]
  Reading: Ch10: Analysis of Social Networks
• 02/11: Analysis of Massive Graphs II
  Slides: [pptx], [pdf]
  Reading: Ch10: Analysis of Social Networks
• 02/16: Large-Scale Machine Learning I
  Slides: [pptx], [pdf]
  Reading: Ch12: Large-Scale Machine Learning
• 02/18: Large-Scale Machine Learning II
  Slides: [pptx], [pdf]
  Reading: Ch12: Large-Scale Machine Learning
• 02/23: Mining Data Streams I
  Slides: [pptx], [pdf]
  Reading: Ch4: Mining data streams (Sect. 4.1-4.3)
• 02/25: Mining Data Streams II
  Slides: [pptx], [pdf]
  Reading: Ch4: Mining data streams (Sect. 4.4-4.7)
• 03/01: Computational Advertising
  Slides: [pptx], [pdf]
  Reading: Ch8: Advertising on the Web
• 03/03: Computational Advertising, comparison between MapReduce-like systems and bulk-synchronous systems
  Slides: [pptx], [pdf]
• 03/08: [Himabindu Lakkaraju, Tim Althoff]: Submodular Optimization
  Slides: [pptx], [pdf]
• 03/10: [Caroline Suen]: Multi-arm Bandits, [Jeff Ullman]: Design of good MapReduce Algorithms
  MapReduce Algorithms slides: [pptx], [pdf]
  Multi-arm Bandits slides: [pptx], [pdf]

(Tentative) List of future lectures and readings

All readings have been derived from the Mining Massive Datasets by J. Leskovec, A. Rajaraman and J. Ullman.

Gradiance

• Quiz Question Examples
  

Recitation sessions documents

• Probability review document
  
• Proof techniques review document
  
• Linear algebra review document (courtesy CS 229)