greedy (locally optimal choices) algorithms make locally optimal choices at each step, hoping to reach a global optimum. You use it when the problem has the greedy choice property: a local optimum leads to a global optimum. Greedy is simpler than DP when it works, but proving correctness matters.
You'll see this in scheduling, selection, and optimization problems. If you can explain these concepts in your own words, you'll solve related problems faster under interview pressure.
