CSEP 590A | Home Logistics Lectures: Wednesday, 6:30-9:20pm, room CSE2 G010. We will also have the class on zoom. You can find links to the Zoom lectures on Ed or Canvas. Public resources: The lecture slides and assignments will be posted online as the course progresses. We are happy for anyone to use these resources, but we cannot grade the work of any students who are not officially enrolled in the class. Grading and evaluation: There will be 10 Colabs (20%), 4 homeworks (40%), and a course project (40%). Students should upload their submissions to GradeScope. (Entry Code: X3WYKY). More information here. Office hours: Information here. Contact: Students should ask all course-related questions on the EdDiscussion forum, where you will also find announcements. For external enquiries, personal matters, or in emergencies, you can email us at csep590a-instructors@cs.washington.edu. Please do not use other emails (such as the TAs' UW email IDs) for questions about the class, as these may not be answered in a timely manner. Deadlines Clarification: All deadlines on Wednesday are at 6pm PT. If they fall on a non Wednesday, then it will be 23:59pm PT. Instructor Tim Althoff Teaching Assistants Ken Gu (Head TA) Dong He Hao Peng Content What is this course about? The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce and Spark as tools for creating parallel algorithms that can process very large amounts of data. Topics include: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommendation Systems, Clustering, Link Analysis, Large scale supervised machine learning, Data streams, Mining the Web for Structured Data. This course is modeled after CS246: Mining Massive Datasets by Jure Leskovec at Stanford University. Reference Text The following text is useful, but not required. It can be downloaded for free, or purchased from Cambridge University Press. Leskovec-Rajaraman-Ullman: Mining of Massive Dataset Prerequisites Students are expected to have the following background: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program (e.g., CS332, CS373 or equivalent are recommended). Good knowledge of Python and Java will be extremely helpful since several assignments will require the use of Spark/Hadoop. Familiarity with basic probability theory (any introductory probability course). Familiarity with writing rigorous proofs (e.g., CS311 or equivalent). Familiarity with basic linear algebra (e.g., Math 308 or equivalent). Familiarity with algorithmic analysis (e.g., CS332/CS373; CS417/CS421 would be more than necessary). Students may refer to the following materials for an overview and review of the expected background. Related questions can be posted on EdDiscussion or during Office Hours. Probability and Proof Techniques Linear Algebra Spark Tutorial(a video is available through Stanford CS246) Recordings of the recitations will be available on Panopto. You will need to login using your UW netid in order to watch the videos. Students may decide to enroll without knowledge of these prerequisites but expect an significant increase in work load to learn these concurrently (e.g. 10 hours per week per missing prerequisite as a rule of thumb). Accessibility & Accommodations Embedded in the core values of the University of Washington is a commitment to ensuring access to a quality higher education experience for a diverse student population. Disability Resources for Students (DRS) recognizes disability as an aspect of diversity that is integral to society and to our campus community. DRS serves as a partner in fostering an inclusive and equitable environment for all University of Washington students. The DRS office is in 011 Mary Gates Hall. Please see the UW resources at: http://depts.washington.edu/uwdrs/current-students/accommodations/. Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy: (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form: (https://registrar.washington.edu/students/religious-accommodations-request/). Schedule Note: Lectures will in person in room CSE2 G010. Zoom links posted on Canvas. Note: Recordings of the lectures will be available on Panopto. Lecture slides will be posted here shortly before each lecture. This schedule is subject to change. Date Description Course Materials Events Deadlines Wed Mar 30 Introduction; MapReduce and Spark [slides] Frequent Itemsets Mining [slides] Course Information: handout Suggested Readings: Ch1: Data Mining Ch2: Large-Scale File Systems and Map-Reduce Ch6: Frequent itemsets Start planning course project [Teams signup form] [Colab 0] [Colab 1] & Assignment 1 [handout, bundle file] out Fri April 1 Recitation: Spark 7:30 - 8:30pm via Zoom (See Ed/Canvas) Tues Apr 5 Recitation: Linear Algebra 7:30 - 8:30pm via Zoom (See Ed/Canvas) Wed Apr 6 Locality Sensitive Hashing [slides] Theory of Locality Sensitive Hashing [slides] Suggested Readings: Ch3: Finding Similar Items (Sect. 3.1-3.4 and 3.5-3.8) Colab 2 [Colab 2] out Colab 0, Colab 1 due Thurs Apr 7 Recitation: Probability and Proof Techniques 7:30 - 8:30pm via Zoom (See Ed/Canvas) Wed Apr 13 Clustering [slides] Dimensionality Reduction [slides] Suggested Readings: Ch7: Clustering (Sect. 7.1-7.4) Ch11: Dimensionality Reduction (Sect. 11.4) Colab 3 [Colab 3] & Assignment 2 out [handout, bundle file] Colab 2 & Assignment 1 due Fri Apr 15 Project Team Signup [Teams signup form] due Wed Apr 20 Recommender Systems I [slides] Recommender Systems II [slides] Suggested Readings: Ch9: Recommendation systems Colab 4 [Colab 4] out Colab 3 due , Project Proposal due (no late periods) Wed Apr 27 PageRank [slides] Link Spam and Introduction to Social Networks [slides] Suggested Readings: Ch5: Link Analysis (Sect. 5.1-5.5) Ch10: Analysis of Social Networks (Sect. 10.1-10.2, 10.6) Colab 5 [Colab 5] & Assignment 3 out [handout, bundle file] Sun May 1 Colab 4 & Assignment 2 due Wed May 4 Community Detection in Graphs [slides] Graphs Representation Learning [slides] Suggested Readings: Ch10: Analysis of Social Networks (Sect. 10.3-10.5, 10.7-10.8) Colab 6 [Colab 6] out Sun May 8 Project Milestone & Colab 5 due (no late periods) Wed May 11 Large-Scale Machine Learning Suggested Readings: Ch12: Large-Scale Machine Learning Colab 7 & Assignment 4 out Sun May 15 Colab 6 & Assignment 3 due Wed May 18 Mining Data Streams Suggested Readings: Ch4: Mining data streams (Sect. 4.1-4.7) Colab 8 out Sun May 22 Colab 7 due Wed May 25 Course Project Meetings Optimizing Submodular Functions Sign up for meeting slots on EdDiscussion Suggested Readings: TimeMachine: Timeline Generation for Knowledge-base Entities by Althoff, Dong, Murphy, Alai, Dang, Zhang. KDD 2015. Colab 9 out Sun May 29 Colab 8 & Assignment 4 due Wed Jun 1 Causal Inference c TBD Colab 9 due & Final Report due & Presentation video due (no late periods) Mon June 6 6:30pm 9:20pm PT (TBD zoom/in person) Virtual Project Presentations
