Let's use the *Memoization* principle here. Let `dp[i][j]` be a Binomial Coefficient C(i,j). First, `dp` initialized with `None`. 

Given `dp[n][k]`:
- if it is None, calculate it as `c(n-1, k-1) + c(n-1, k)`
- if it is a base case: `k==0 or k==n`, then `dp[n][k] = 1`
- else return `dp[n][k]`

**Note** that structure `dp` depends on `n`, and you must use it for defined `n`.

n = 200
dp = [[None for _ in range(n+1)] for _ in range(n+1)]
def c(n, k):
  if k==0 or k==n:
    dp[n][k] = 1
        
  if dp[n][k] == None:
    dp[n][k] = c(n-1, k)+c(n-1, k-1)
        
  return dp[n][k]

print(c(3, 2))
print(c(10, 4))
print(c(11, 5))
print(c(144, 7))

This course introduces a Dynamic Programming approach for users familiar with the syntax and data structures of the Python programming language. Dynamic programming is a necessary topic for a technical interview, as are Binary Search, Sorting Algorithms, etc. Be prepared to solve a few problems on your own. Use the hints or Solutions section if you get stuck.

Brief overview: what is Dynamic Programming, and what are the main principles.

Solve these problems using DP principles.

See the explained solutions for the problems.

Problem A. Binomial Coefficient