Algorithms for Big Data, Fall 2020. (original) (raw)

Instructor: David Woodruff

Lecture time: Thursdays, 16:00-19:00, on Zoom, starting Sep 10

TA: Praneeth Kacham

David's office hours: Mondays, 10:30-11:30am ET, or by appointment

Praneeth's recitation: Fridays, 8-9pm ET, on Zoom, starting Sep 11

Praneeth's office hours: Wednesdays, 8-9pm ET, or by appointment

Piazza site: piazza.com/cmu/fall2020/15859/home

Grading Latex Course Description Lectures Recitations Problem sets References

Grading

Grading is based on problem sets, scribing a lecture, and a presentation/project. There will be no exams. General information about the breakdown for the grading is available here: grading.pdf

Latex

Homework solutions, scribe notes, and final projects must be typeset in LaTeX. If you are not familiar with LaTeX, see this introduction.

A template for your scribe notes is here: template.tex

Lectures

Recitations

See links/handouts on piazza

Problem sets

Course Description

With the growing number of massive datasets in applications such as machine learning and numerical linear algebra, classical algorithms for processing such datasets are often no longer feasible. In this course we will cover algorithmic techniques, models, and lower bounds for handling such data. A common theme is the use of randomized methods, such as sketching and sampling, to provide dimensionality reduction. In the context of optimization problems, this leads to faster algorithms, and we will see examples of this in the form of least squares regression and low rank approximation of matrices and tensors, as well as robust variants of these problems. In the context of distributed algorithms, dimensionality reduction leads to communication-efficient protocols, while in the context of data stream algorithms, it leads to memory-efficient algorithms. We will study some of the above problems in such models, such as low rank approximation, but also consider a variety of classical streaming problems such as counting distinct elements, finding frequent items, and estimating norms. Finally we will study lower bound methods in these models showing that many of the algorithms we covered are optimal or near-optimal. Such methods are often based on communication complexity and information-theoretic arguments.

References

One recommended reference book is the lecturer's monograph Sketching as a Tool for Numerical Linear Algebra. Some videos from a shorter version of this course I taught are available here: videos . Note that mine start on 27-02-2017.

This course was previously taught at CMU in both Fall 2017 and Fall 2019

Materials from the following related courses might be useful in various parts of the course:

Intended audience: The course is indended for both graduate students and advanced undegraduate students with mathematical maturity and comfort with algorithms, discrete probability, and linear algebra. No other prerequisites are required.

Maintained by David Woodruff