Handouts

Assignments

Gradiance (no late periods allowed, class token 79D9D7F3):

GHW 1: Due on 1/25 at 11:59pm (Pacific Standard time).
GHW 2: Due on 1/25 at 11:59pm.
GHW 3: Due on 2/01 at 11:59pm.
GHW 4: Due on 2/08 at 11:59pm.
GHW 5: Due on 2/15 at 11:59pm.
GHW 6: Due on 2/22 at 11:59pm.
GHW 7: Due on 3/01 at 11:59pm.
GHW 8: Due on 3/08 at 11:59pm.
GHW 9: Due on 3/15 at 11:59pm.

Homeworks (2 late periods allowed):

HW0 (Spark tutorial) to help you set up Spark: Due on 1/25 at 11:59pm. Solutions: [code]
HW1: Due on 1/25 at 11:59pm. Submission Templates: [pdf | tex]. Solutions: [pdf | code]
HW2: Due on 2/08 at 11:59pm. Submission Templates: [pdf | tex]. Solutions: [pdf | code]
HW3: Due on 2/22 at 11:59pm. Submission Templates: [pdf | tex]. Solutions: [pdf | code]
HW4: Due on 3/08 at 11:59pm. Submission Templates: [pdf | tex]. Solutions: [pdf | code]
Final exam with solutions. For problem 1, see the code in [Python] [Java] [Scala].

Lecture notes (Future Schedule is tentative)

01/09: Introduction; MapReduce
Slides: [pdf][pptx]
Reading: Ch1: Data Mining and Ch2: Large-Scale File Systems and Map-Reduce (Sect. 2.1-2.4)
01/11: Frequent Itemsets Mining
Slides: [pdf] [pptx]
Reading: Ch6: Frequent itemsets
01/16: Locality-Sensitive Hashing I
Slides: [pdf] [pptx]
Reading: Ch3: Finding Similar Items (Sect. 3.1-3.4)
01/18: Locality-Sensitive Hashing II
Slides: [pdf] [pptx]
Reading: Ch3: Finding Similar Items (Sect. 3.5-3.8)
01/23: Clustering
Slides: [pdf] [pptx]
Reading: Ch7: Clustering (Sect. 7.1-7.4)
01/25: Dimensionality Reduction
Slides: [pdf] [pptx]
Reading: Ch11: Dimensionality Reduction (Sect. 11.4)
01/30: Recommender Systems I
Slides: [pdf] [pptx]
Reading: Ch9: Recommendation systems
02/01: Recommender Systems II
Slides: [pdf] [pptx]
Reading: Ch9: Recommendation systems
02/06: PageRank
Slides: [pdf] [pptx]
Reading: Ch5: Link Analysis (Sect. 5.1-5.3, 5.5)
02/08: Link Spam and Introduction to Social Networks
Slides: [PageRank.pdf] [PageRank.pptx], [graph.pdf] [graph.pptx]
Reading: Ch5: Link Analysis (Sect. 5.4)
Reading: Ch10: Analysis of Social Networks (Sect. 10.1-10.2, 10.6)
02/13: Social Networks: Community Detection and Trawling
Slides: [pdf] [pptx]
Reading: Ch10: Analysis of Social Networks (Sect. 10.3-10.5)
02/15: Algorithms on Large Graphs
Slides: [pdf] [pptx]
Reading: Ch10: Analysis of Social Networks (Sect. 10.7-10.8)
02/20: Large-Scale Machine Learning I
Slides: [pdf], [pptx]
Reading: Ch12: Large-Scale Machine Learning
02/22: Large-Scale Machine Learning II
Slides: [pdf], [pptx]
Reading: Ch12: Large-Scale Machine Learning
02/27: Mining Data Streams I
Slides: [pdf] [pptx]
Reading: Ch4: Mining data streams (Sect. 4.1-4.3)
03/01: Mining Data Streams II
Slides: [pdf] [pptx]
Reading: Ch4: Mining data streams (Sect. 4.4-4.7)
03/06: Computational Advertising
Slides: [pdf] [pptx]
Reading: Ch8: Advertising on the Web
03/08: Learning through Experimentation
Slides: [pdf] [pptx]
Reading: A Contextual-Bandit Approach to Personalized News Article Recommendation by Li, Chu, Langford, Schapier. WWW 2010.
03/13: Optimizing Submodular Functions
Slides: [pdf] [pptx]
Reading: Turning Down the Noise in the Blogosphere by El-Arini, Veda, Shahaf, Guestrin. KDD 2009.
03/15: Review
Slides: [pdf] [pptx]

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

Recitation sessions documents

Probability review notes (courtesy CS 229)
Probability review slides
Proof techniques review (TBA)
Linear algebra review (courtesy CS 229)
Linear algebra review slides (TBA)
Linear algebra, probability, and proof techniques (from CS224W)

Spark tutorial slides