Free Textbooks: Computer Science

Free text­books (aka open text­books) writ­ten by knowl­edgable schol­ars are a rel­a­tive­ly new phe­nom­e­non. Below, find a meta list of Free Com­put­er Sci­ence Text­books, part of our larg­er col­lec­tion 200 Free Text­books: A Meta Col­lec­tion. Also see our online col­lec­tion, 1,700 Free Online Cours­es from Top Uni­ver­si­ties.

• A Byte of Python, by Swa­roop C H
• A First Course in Elec­tri­cal and Com­put­er Engi­neer­ing by Louis Scharf, Col­orado State
• Arti­fi­cial Intel­li­gence: Foun­da­tions of Com­pu­ta­tion­al Agents by David Poole and Alan Mack­worth, Uni­ver­si­ty of British Colum­bia
• Bits, Sig­nals, and Pack­ets: An Intro­duc­tion to Dig­i­tal Com­mu­ni­ca­tions and Net­works by Hari Bal­akr­ish­nan, Christo­pher Ter­man, and George Vergh­ese, MIT
• Code Like a Python­ista: Idiomat­ic Python, by David Goodger
• Com­pu­ta­tion­al Geom­e­try by Nicholas M. Patrikalakis, Takashi Maekawa, MIT
• Dig­i­tal Cir­cuit Projects: An Overview of Dig­i­tal Cir­cuits Through Imple­ment­ing Inte­grat­ed Cir­cuits by Charles W. Kann, Get­tys­burg Col­lege
• Dive into Python, by Mark Pil­grim
• Foun­da­tions of Com­put­er Sci­ence by Al Aho (Colum­bia) and Jeff Ull­man (Stan­ford)
• High Per­for­mance Com­put­ing by Charles Sev­er­ance, Uni­ver­si­ty of Michi­gan
• How to Design Pro­grams: An Intro­duc­tion to Com­put­ing and Pro­gram­ming, Mul­ti­ple Authors
• How to Think Like a Com­put­er Sci­en­tist: C ++ ( PDF) by Allen B. Downey, Olin Col­lege
• How to Think Like a Com­put­er Sci­en­tist: Java by Allen B. Downey, Olin Col­lege
• How to Think Like a Com­put­er Sci­en­tist: Python by Allen B. Downey, Olin Col­lege
• Imple­ment­ing a One Address CPU in Logisim by Charles W. Kann III, Get­tys­burg Col­lege
• Infor­ma­tion Tech­nol­o­gy and the Net­worked Econ­o­my by Patrick McK­e­own, Uni­ver­si­ty of Geor­gia
• Infor­ma­tion The­o­ry, Infer­ence, and Learn­ing Algo­rithms by David MacK­ay, Cam­bridge
• Intro­duc­tion To MIPS Assem­bly Lan­guage Pro­gram­ming by Charles W. Kann III, Get­tys­burg Col­lege
• Learn Python the Hard Way, by Zed A. Shaw
• Neur­al Net­works and Deep Learn­ing, by Michael Nielsen, Research Fel­low at the Recurse Cen­ter
• Pat­terns for Begin­ning Pro­gram­mers (with Exam­ples in Java), by David Bern­stein, James Madi­son Uni­ver­si­ty
• Phi­los­o­phy of Com­put­er Sci­ence by William J. Rapa­port, Uni­ver­si­ty at Buf­fa­lo, The State Uni­ver­si­ty of New York
• Prin­ci­ples of Com­put­er Sys­tem Design: An Intro­duc­tion (Part II) by Jerome Saltzer and M. Frans Kaashoek.
• Prin­ci­ples of Pro­gram­ming Lan­guages by Grad Stu­dents, Johns Hop­kins
• Pro­gram­ming Lan­guages: Appli­ca­tion and Inter­pre­ta­tion by Dr. Shri­ram Krish­na­murthi, Brown Uni­ver­si­ty.
• Pro­log and Nat­ur­al-Lan­guage Analy­sis by Fer­nan­do C. N. Pereira and Stu­art M. Shieber, U Penn & Har­vard
• Python for Infor­mat­ics: Explor­ing Infor­ma­tion by Charles Sev­er­ance, Uni­ver­si­ty of Michi­gan
• Struc­ture and Inter­pre­ta­tion of Com­put­er Pro­grams by Jer­ry Suss­man & Julie Suss­man
• Teach Your­self Word­Per­fect Mac by John Rethorst
• The Prince­ton Bit­coin Text­book by Arvind Narayanan (Prince­ton) and col­leagues

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