Algorithms Course Materials

This class is ultimately about learning two skills that are crucial for computer scientists: how to think about algorithms and how to talk about algorithms. Along the way, you'll pick up a bunch of algorithmic facts—mergesort runs in O(n log n) time; the amortized time to search in a splay tree is O(log n); greedy algorithms usually don't produce optimal solutions; the traveling salesman problem is NP-hard—but these aren't the point of the course. You can always look up mere facts in a textbook or on the web, provided you have enough intuition and experience to know what to look for. That's why we let you bring cheat sheets to the exams; we don't want you wasting your study time trying to memorize all the facts you've seen. You'll also practice a lot of algorithm design and analysis skills—finding useful (counter)examples, developing induction proofs, solving recurrences, using big-Oh notation, using probability, giving problems crisp mathematical descriptions, and so on. These skills are very useful, but they aren't really the point of the course either. At this point in your educational career, you should be able to pick up those skills on your own, once you know what you're trying to do.

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