COMPSCI 514: Algorithms for Data Science (Spring 2020 -- Now Online!)

Time: Tue/Thurs 11:30am-12:45pm
Location: Lecture Zoom
Professor: Cameron Musco
Teaching Assistants:
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:
Related Classes: You may also find some helpful reference material in these similar classes taught at other universities:
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.
Exams: We will have an in class midterm exam (March 12) along with a final (May 6th, 2:00pm-4:00pm).
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: