diff --git a/data_structures_and_algorithms/greedy/README.md b/data_structures_and_algorithms/greedy/README.md
index e196fb9..ec2bb3a 100644
--- a/data_structures_and_algorithms/greedy/README.md
+++ b/data_structures_and_algorithms/greedy/README.md
@@ -1,5 +1,42 @@
 # Greedy Algorithms
 
+At each step choose best option.
+
+Making change with the minimum number of coins
+
+```
+def minCoins(k):
+  coins = [1, 5, 10, 25] #pennies, nickles, dimes, quarters
+  n = len(coins) #we have 4 different denominations of coins. We can use as many from each of them.
+  res = []
+  i = n - 1 #start at last index
+  while(i >= 0  and k >= 0):
+    if k >= coins[i]:
+      k -= coins[i]
+      res.append(coins[i])
+    else:
+      i -= 1
+  return res
+```
+
+In ML, decision trees are split in a greedy manner to maximise information gain (reduction in entropy based on feature chosen) at each split. For every feature, we evaluate all features, then in a greedy fashion, choose the feature with the best information gain to split on next. This repeats until we end up with leaf nodes.
+```
+info_gains = [getInfoGains(feature) for feature in features]
+best_feature_index = np.argmax(info_gains)
+best_feature = features[best_feature_index]
+```
+For optimization problem, use greedy when there's an obvious set of choices to select from and it's easy to know what the appropriate choice is.
+
+Note: It is easy to reason about a greedy algorithm recursively, but then implement it later iteratively for better memory performance.
+
+## Greedy Algorithms vs Dynamic Programming
+
+Greedy Algorithms always does local optimization while dynamic programming is always guaranteed to find the globally optimal solution because it exhaust the search space.
+
+```
+0/1 Knapsack problem
+```
+
 | Problem Link | Platform | Level | Solution (in Python) Link |
 | --- | --- | --- | --- |
 | [Maximum Subarray](https://leetcode.com/problems/maximum-subarray/) | LeetCode | Medium