CPSC 320 is about several broad categories of problems and problem-solving techniques (such as greedy algorithms, divide-and-conquer algorithms, dynamic programming, and the class of NP-complete problems) and gaining the tools and experience necessary to judge how a new problem might fit one of these categories, how to approach solving the problem, and how to analyze and adjust your solution.

At the end of the course, you will be able to:

1. Recognize which algorithm design technique(s), such as divide and conquer, prune and search, greedy strategies, or dynamic programming was used in a given algorithm.
2. Select and judge several promising paradigms and/or data structures (possibly slightly modified) for a given problem by analyzing the problem's properties.
3. Implement a solution to a problem using a specified algorithm design paradigm, given sufficient information about the form of that problem's solution
4. Select, judge and apply promising mathematical techniques (such as asymptotic notations, recurrence relations, amortized analysis and decision trees) to establish reasonably tight upper and lower bounds on the running time of algorithms.
5. Recognize similarities between a new problem and some of the problems you have encountered, and judge whether or not these similarities can be leveraged towards designing an algorithm for the new problem.

A more detailed list of topics-level learning goals can be found here.