Dynamic Programming
 - Recursion + memoization = dyanamic programming
 - Ability to convert recursive algorithm into iterative algorithm
   to avoid recomputing solutions
 - Run time and space analysis
 - Reducing space if only value needed

Greedy Algorithms
 - exchange argument based proofs for greedy algorithm

Minimum Spanning Trees
 - definition of problem
 - cut and cycle properties, safe and unsafe edges
 - correctness of MST algorithms via safe/unsafe characterization
 - Kruskal, Prim, and their implementation and running time
 - Union Find data structure and knowledge of implementations discussed
   in lecture notes.
 - Basics of amortized analysis

Basics of randomized algorithms
 - definition of a randomized algorithm
 - basics of discrete probability, especially expectation
 - randomized quick sort and selection
 - basic knowledge of hashing but no need to know analysis of
   universal hashing.

Basics of network flow
 - definitions of flows/cuts
 - Ford-Fulkerson algorithm for finding a maximum flow
   via augmenting paths in residual graph
 - Proof of maxflow-mincut theorem via FF algorithm
 - Integrality of flow when capacities are integer
 - Knowledge of polynomial time variants of FF and their running time.
 - Finding a mincut through a max flow


These topics cover what we discussed in lecture from midterm 1 to
this Tuesday's lecture (which I will finish in the first part
of this Thursday). Applications of network flows are not on
the midterm.