Logistics

• Lectures: Tuesdays & Thursdays, 10:00-11:20am, CSE2 room G010.
• 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. (Activation Code: 7G5RY2). More information here.
• Office hours: Information here.
• Contact: Students should ask all course-related questions on the ed forum, where you will also find announcements. For external enquiries, personal matters, or in emergencies, you can email us at cse547-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 are at 23:59pm PT.

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 ed or during Office Hours.

• Probability and Proof Techniques [web link]
• Linear Algebra [web link]
• Spark Tutorial (a video is available through Stanford CS246)

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 nstructoensurrng 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 the accomodations website.

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 the religious accommodations policy website.
Accommodations must be requested within the first two weeks of this course using the religious accommodations request form.