Time: Tue/Thurs 11:30am-12:45pm

Location: Lecture Zoom

Professor: Cameron Musco

• Email: cmusco at cs dot umass dot edu.
• Office: CS 234
• Office Hours: Tue 12:45pm-2:00pm (right after class) at Office Hours Zoom.

Course Description: With the advent of social networks, ubiquitous sensors, and large-scale computational science, data scientists must deal with data that is massive in size, arrives at blinding speeds, and often must be processed within interactive or quasi-interactive time frames. This course studies the mathematical foundations of big data processing, developing algorithms and learning how to analyze them. We explore methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. Course was previously COMPSCI 590D. 3 credits.

Prerequisites: The undergraduate prerequisites are COMPSCI 240 (Probability) and COMPSCI 311 (Algorithms). This is a theoretical course with an emphasis on algorithm design, correctness proofs, and analysis. Aside from a general background in algorithms, a strong mathematical background, particularly in linear algebra and probability is required. If you are a masters student with a limited background in either of these subjects, please email me at the start of the semester.

Textbooks: This is no official textbook for this class. We will use some material from:

• Foundations of Data Science, Avrim Blum, John Hopcroft and Ravi Kannan.
• Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman and Jeff Ullman.

Related Classes: You may also find some helpful reference material in these similar classes taught at other universities:

• The Modern Algorithmic Toolbox, Gregory Valiant at Stanford.
• Sketching Algorithms for Big Data, Piotr Indyk and Jelani Nelson at MIT/Harvard.
• Algorithmic Techniques for Big Data, Moses Charikar at Stanford.
• COMPSCI 514 last semester (Fall 2019).

Piazza: We will use Piazza for class discussion and questions. Sign up here. Our goal is for students to answer each others' questions on Piazza as much as the TAs and instructor do. Thus, we encourage good question answering with extra credit (see extra credit policy below).

Homework: Problem sets can be completed in groups of up to three students. If you work in a group, you submit a single problem set together. You may talk to people not in your group about the problem sets at a high level, but may not work through the detailed solutions together, write them up together, etc. We very strongly encourage you to work in a three person group, as it will give an advantage in doing the problem sets. At the beginning of the semester we will make a Piazza post where you can look for teammates.

• Problem set submissions will be via Gradescope. If working in a group, only one member of each group should submit the problem set, marking the other members in the group as part of the submission in Gradescope.
• The entry code for Gradescope is MP3VVK. Please sign up and complete the Gradescope consent poll in Piazza by 1/30.
• No late homework submissions will be accepted unless there are extenuating circumstances, approved by the instructor before the deadline.
• I strongly encourage students to type up problem sets using either Latex or Markdown. A Latex template for problem sets can be downloaded here. For editing Markdown, I use Typora, which supports Latex-style math equations (see here). While they may seem cumbersome at first, these tools will save you a lot of time in the long run!

Exams: We will have an in class midterm exam (March 12) along with a final (May 6th, 2:00pm-4:00pm).

Grading:

• Problem Sets (4 total): 40%, weighted equally.
• Midterm: 30%.
• Final: 30%.

Extra Credit: Students may be awarded up to 5% extra credit for in class and Piazza participation (asking good clarifying questions in class and on Piazza, answering instructors questions in class, answering other students' questions on Piazza, etc.).

Disability Services: UMass Amherst is committed to making reasonable, effective, and appropriate accommodations to meet the needs to students with disabilities and help create a barrier-free campus. If you have a documented disability on file with Disability Services, you may be eligible for reasonable accommodations in this course. If your disability requires an accommodation, please notify me within the first two weeks of the course so that we may make arrangements in a timely manner.

Helpful UMass Resources:

• Academic Honesty Policy
• English as a Second Language (ESL) Program
• Center for Counseling and Psychological Health