Current offering (Fall 2020)

• Sketching Algorithms

Related previous offerings

• Sketching Algorithms for Big Data: Piotr Indyk (MIT), Jelani Nelson (Harvard).
• Sketching, Streaming, and Sub-linear Space algorithms: Piotr Indyk (MIT).
• Algorithms for Big Data: Jelani Nelson (Harvard).

Related courses at other schools

• Algorithmic Techniques for Big Data: Moses Charikar (Stanford).
• Algorithmic Techniques for Big Data Analysis: Barna Saha (University of Minnesota Twin Cities).
• Algorithmic Techniques for Massive Data: Alexandr Andoni (Columbia).
• Algorithms for Big Data: Chandra Chekuri (UIUC).
• Algorithms for Big Data: David Woodruff (CMU).
• Algorithms for Big Data: Grigory Yaroslavtsev (UPenn).
• Algorithms for Modern Data Models: Ashish Goel (Stanford).
• Data Streams Algorithms: Andrew McGregor (UMass Amherst).
• Data Stream Algorithms: Amit Chakrabarti (Dartmouth).
• Dealing with Massive Data: Sergei Vassilvitskii (Columbia).
• Mining Massive Data Sets: Jure Leskovec (Stanford).
• Models of Computation for Massive Data: Jeff Phillips (Utah).
• Randomized Algorithms for Matrices and Data: Michael Mahoney (UC Berkeley).
• Seminar on Data Streams: Hung Ngo, Atri Rudra (Buffalo).
• Sublinear Algorithms: Erice Price (UT Austin).
• Sublinear algorithms: Piotr Indyk, Ronitt Rubinfeld (MIT).
• Sublinear and streaming algorithms: Paul Beame (University of Washington).
• The Modern Algorithmic Toolbox: Tim Roughgarden, Gregory Valiant (Stanford).
• A list of compressed sensing courses, compiled by Igor Carron.