- Email: cmusco at cs dot umass dot edu.
- Office: CS 234
- Office Hours: Tuesday 2:30pm-3:30pm (directly after class) in CS 234.
- How to Contact: If you need to chat or schedule an individual meeting, you can reach out over email, via a Piazza message, or in person, after class or during office hours.

- An La
- Email: anla at umass dot edu
- Office Hours: Wednesday 12:30pm-1:30pm in CS207 Cube 1. Friday 4:30pm-5:30pm, over Zoom.
- Mohit Yadav
- Email: ymohit at cs dot umass dot edu
- Office Hours: Monday 9:30am-10:30am, Thursday 9:30am-10:30am. Over Zoom.
- Forsad Al Hossein
- Email: falhossain at cs dot umass dot edu
- Office Hours: Monday 4:00pm-5:00pm, in CS207. Tuesday 9:00am-10:00am, over Zoom.

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

- 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 year (Fall 2021).

- Problem Sets (5 total): 40%, weighted equally.
- Weekly Quizzes: 10%, weighted equally, lowest score dropped.
- Midterm: 25%.
- Final: 25%.

- 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
`2KBPNG`

. - 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!

- Asking good clarfiying questions and answering questions during lecture.
- Actively participating in office hours.
- Asking good clarfiying questions and answering other students' or instructor questions on Piazza.
- Posting helpful links on Piazza, e.g., resources that cover class material, research articles related to the topics covered in class, etc.

I understand that people have different learning needs, home situations, etc. If something isn’t working for you in the class, please reach out and let’s try to work it out.

- Students will learn about modern tools for data processing, including random sampling and hashing, low-memory streaming algorithms, linear and non-linear dimensionality reduction, spectral graph theory, and continuous optimization. A major goal is to be familiar at a high level with a breadth of algorithmic tools beyond combinatorial algorithms, which are the main focus of most undergraduate algorithms courses.
- Through problem sets, students will develop the ability to apply and modify these algorithmic tools to tackle new problems, beyond those discussed in class. They will strengthen their ability to think creatively about algorithmic problems and push beyond known approaches, to develop solutions of their own.
- Through assessments that emphasize formal proofs, students will strengthen their ability to formulate problems mathematically and analyze them rigorously.
- Through algorithmic problems, students will practice applying fundamental tools in probability theory and linear algebra, which are broadly applicable in data science and machine learning. These include concentration bounds and methods for decomposing complex random variables, eigendecomposition, orthogonal projection, important matrix identities, and fundamentals of high-dimensional geometry and random matrix theory.