Algorithms for Big Data: A Free Course from Harvard

From Har­vard pro­fes­sor Jelani Nel­son comes “Algo­rithms for Big Data,” a course intend­ed for grad­u­ate stu­dents and advanced under­grad­u­ate stu­dents. All 25 lec­tures you can find on Youtube here.

Here’s a quick course descrip­tion:

“Big data is data so large that it does not fit in the main mem­o­ry of a sin­gle machine, and the need to process big data by effi­cient algo­rithms aris­es in Inter­net search, net­work traf­fic mon­i­tor­ing, machine learn­ing, sci­en­tif­ic com­put­ing, sig­nal pro­cess­ing, and sev­er­al oth­er areas. This course will cov­er math­e­mat­i­cal­ly rig­or­ous mod­els for devel­op­ing such algo­rithms, as well as some prov­able lim­i­ta­tions of algo­rithms oper­at­ing in those mod­els. Some top­ics we will cov­er include”:

• Sketch­ing and Stream­ing. Extreme­ly small-space data struc­tures that can be updat­ed on the fly in a fast-mov­ing stream of input.
• Dimen­sion­al­i­ty reduc­tion. Gen­er­al tech­niques and impos­si­bil­i­ty results for reduc­ing data dimen­sion while still pre­serv­ing geo­met­ric struc­ture.
• Numer­i­cal lin­ear alge­bra. Algo­rithms for big matri­ces (e.g. a user/product rat­ing matrix for Net­flix or Ama­zon). Regres­sion, low rank approx­i­ma­tion, matrix com­ple­tion, …
• Com­pressed sens­ing. Recov­ery of (approx­i­mate­ly) sparse sig­nals based on few lin­ear mea­sure­ments.
• Exter­nal mem­o­ry and cache-obliv­i­ous­ness. Algo­rithms and data struc­tures min­i­miz­ing I/Os for data not fit­ting on mem­o­ry but fit­ting on disk. B‑trees, buffer trees, mul­ti­way merge­sort.

“Algo­rithms for Big Data” will be added to our col­lec­tion of Free Com­put­er Sci­ence Cours­es, a sub­set of our col­lec­tion, 1,700 Free Online Cours­es from Top Uni­ver­si­ties.

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