Coin Change

Naive recursive Fibonacci computes `fib(40)` in seconds, `fib(50)` in minutes, and gives up on `fib(60)`, all because it recomputes the same subproblems exponentially many times. Cache the result of each `fib(k)` the first time you compute it and the same recursion runs in linear time. That single change, remembering answers, is the entire content of dynamic programming. **Dynamic Programming (Intro)** turns that observation into a complete problem-solving framework. You will identify overlapping subproblems and optimal substructure (the two properties a problem must have for DP to apply), and master both approaches: top-down memoization (recursion plus a cache) and bottom-up tabulation (iteratively filling a table). Classic 1D problems include Fibonacci, climbing stairs, coin change, house robber, and a first look at Kadane's algorithm. The lesson teaches you to define state precisely ("what does `dp[i]` represent?"), write the transition ("how does `dp[i]` follow from earlier states?"), set base cases, and apply rolling-variable space optimization that drops `O(n)` to `O(1)`. In **Recursion Fundamentals**, you treated each recursive call as a stack frame. Memoization just attaches a cache so identical inputs return immediately. Next, **Bit Manipulation (Intro)** turns to a different toolkit, where bitwise operators give elegant `O(1)` solutions.