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.

Dynamic Programming

Problem A. Binomial Coefficient

Problem A. Binomial Coefficient