- Illinois course materials
- Lecture notes, lecture videos, slides, lab handouts, homeworks, and exams are available for several past semesters of algorithms classes at Illinois. The TV emoji 📺 indicates classes with freely available lecture videos.
- Course materials elsewhere
- Over the last decade, it has sadly become much less common for instructors to maintain course materials on the open web. Most instructors (or perhaps more accurately, most universities) either lock their course materials inside walled gardens (“learning management systems”) or simply delete them when the course is over. Here are some useful exceptions.
— homeworks (without solutions), lab problems (with solutions)
current semester?) — notes/slides for all semesters, recitation worksheets for some semesters, homeworks only for the current semester
Fall 2017) — lecture notes for most semesters, homeworks for all
Fall 2011) — notes, videos, and problem sets
Stanford (Summer 2013) — slides and problem sets
- UC Davis — videos on iTunes
Washington (several quarters) — notes/slides, sporadic problem sets. Primarily focuses on randomized algorithms, especially after about 2015.
Coursera, EdX and Udacity all offer complete algorithms courses, with videos, readings, and automatically graded exercises. By necessity, these courses tend to focus more on implementation and less on proofs and open-ended design than either CS 374 or CS 473.
I need to update this list, becuase MOOCs are a moving target.
- Udacity MOOCs are always freely available.
- EdX MOOCs are available only for enrolled students when the class is in session.
MicroMasters Program in Algorithms and Data Structures,
a series of eight MOOCs taught by Daniel Kane, Pavel Pevzner, and others,
loosely based on courses at UC San Diego
Algorithm Design and Analysis,
taught by Sampath Kannan at Penn
Advanced Algorithmics and Graph Theory with Python,
taught by Vincent Gripon, Patrick Meyer, Nicolas Farrugia,
Carlos Eduardo Rosar Kos Lassance,
and Ghouti Boukli Hacene at Institut Mines-Télécom
- Algorithms and Data Structures, taught by Sari Kulthm at Microsoft
- Algorithms, taught by Deepak Phatak, Nagesh Karmali, Ajit Diwan, and Ganesh Ramakrishnan at IIT Bombay
- Coursera MOOCs are also available only for enrolled students when the class is in session, and they are in session less often than EdX MOOCs.
- Algorithms Part 1 and Part 2, taught by Robert Sedgewick and Kevin Wayne, loosely based on undergraduate algorithms classes at Princeton.
- Analysis of Algorithms, taught by Robert Sedgewick, loosely based on a more advanced class at Princeton.
- Coursera hosts several other algorithms “MOOCs”, but they all require a $49/month subscription to access, unless all you want are the videos. Pfft. Please.
- Khan Academy also hosts a free series of algorithms tutorials by Tom Cormen and Devin Balkcom.
- Free Online Textbooks
- Hopefully this list will continue to grow.
- Dead Trees
- For students who prefer an offline reference made of bleached and stained cellulose, we recommend the following textbooks. The campus bookstore probably doesn't have them, but you can find them cheaper online anyway. And they're all in the library. You remember that big brick building with the coffee shop and the study tables in it? Yeah, believe it or not, they have actual books in the basement!
You can buy paper copies of Jeff's book on Amazon, if you're into that sort of thing.
Algorithms by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani (McGraw-Hill, 2006). Based on the undergraduate algorithms course at Berkeley. Drafts of individual chapters (0,
10) are freely available. (Thanks, Umesh!)
Introduction to Algorithms (3rd edition) by Tom Cormen, Charles Leiserson, Ron Rivest, and Cliff Stein (MIT Press/McGraw-Hill, 2009). Based on algorithms classes at MIT, and thick enough to stun an ox. A significant fraction of this book has been transcribed into Wikipedia.
- There are several other algorithms and theory of computation textbooks I'd love to recommend, but not at the ridiculous prices that their publishers charge. Shame on you, Pearson; fuck you, Cengage.
- For review of prerequisite material, we strongly recommend the following online resources. (This stuff is also covered in several dead-tree textbooks, but really, why bother?)
- Programming contests
- ...which (at least at the advanced levels) are really algorithm design contests where you also happen to write some code.
We'll add more links here as we discover them. Suggestions are welcome!