Fix typo

Fix typos introduces more

pages/CSE347/CSE347_L5.md

@@ -6,7 +6,7 @@
 - In general, we can design an algorithm to map instances of a new problem to instances of known solvable problem (e.g., max-flow) to solve this new problem!
 - Mapping from one problem to another which preserves solutions is called reduction.
 
-## Reduction: Basic Idea
+## Reduction: Basic Ideas
 
 Convert solutions to the known problem to the solutions to the new problem
 

pages/CSE347/CSE347_L7.md

@@ -45,7 +45,7 @@ Assumption: No clause contains both a literal and its negation.
 
 Need to: construct $S$ of positive numbers and a target $t$
 
-Idea of construction:
+Ideas of construction:
 
 For 3-SAT instance $\Psi$:
 
@@ -276,7 +276,7 @@ Consider an instance of SSS: $\{ a_1,a_2,\cdots,a_n\}$ and sum $b$. We can creat
 
 Then we prove that the scheduling instance is a "yes" instance if and only if the SSS instance is a "yes" instance.
 
-Idea of proof:
+Ideas of proof:
 
 If there is a subset of $\{a_1,a_2,\cdots,a_n\}$ that sums to $b$, then we can schedule the jobs in that order on one machine.
 

pages/CSE347/CSE347_L9.md

@@ -38,7 +38,7 @@ Answer: The adversary can make the runtime of each operation $\Theta(n)$ by simp
 
 We don't want the adversary to know the hash function based on just looking at the code.
 
-Idea: Randomize the choice of the hash function.
+Ideas: Randomize the choice of the hash function.
 
 ### Randomized Algorithm
 
@@ -57,7 +57,7 @@ $$O(n)=E[T(n)]$$ or some other probabilistic quantity.
 
 #### Randomization can help
 
-Idea: Randomize the choice of hash function $h$ from a family of hash functions, $H$.
+Ideas: Randomize the choice of hash function $h$ from a family of hash functions, $H$.
 
 If we randomly pick a hash function from this family, then the probability that the hash function is bad on **any particular** set $S$ is small.