Logistics

• Lectures: are on Tuesday/Thursday 3:00-4:20 PM PDT in person in the NVIDIA Auditorium.
• Lecture Videos: are available on Canvas for all the enrolled Stanford students. You can also check our past Coursera MOOC.
• 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.
• Contact: Students should ask all course-related questions on Ed, where you will also find all the announcements. For external enquiries, personal matters, or in emergencies, you can email us at cs246-win2324-staff@lists.stanford.edu.
• Academic accommodations: If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations.

Content

What is this course about? [Info Handout]

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, Web Advertising.

Previous offerings

The previous version of the course is CS345A: Data Mining which also included a course project. CS345A has now been split into two courses, CS246 and CS341.

You can access class notes and slides of previous versions of the course here:

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., CS107 or CS145 or equivalent are recommended).
• Good knowledge of Java and Python will be extremely helpful since most assignments will require the use of Spark.
• Familiarity with basic probability theory (CS109 or Stat116 or equivalent is sufficient but not necessary).
• Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103).
• Familiarity with basic linear algebra (e.g., any of Math 51, Math 103, Math 113, CS 205, or EE 263 would be much more than necessary).
• Familiarity with algorithmic analysis (e.g., CS 161 would be much more than necessary).

The recitation sessions in the first weeks of the class will give an overview of the expected background.

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