Many of these lectures are based on your book Algorithm Design by Kleinberg and Tardos. See it for more detail.

Tentative Schedule:

#	Topics	Links & Reading
1	Case Study: Minimum Spanning tree Big ideas: Distinction between Problems and Algorithms Prim's Minimum spanning tree algorithm Graphs, linked lists, arrays, structures Priority queues, d-Heaps	AD Ch. 1, 2.5, 4.5, 3.1 Lecture 0 - Jan 14 - Introduction Lecture 1 - Jan 16 - Prim's Minimum Spanning Tree Lecture 2 - Jan 18 - MST Data Structures: Heaps & Graphs
2	Intro to Python Kruskal's MST algorithm Union-Find data structures	AD Ch. 2, 4.6 Lecture 3 - Jan 21 and 22 - Kruskal's MST & Union-Find Lecture 4 - Jan 25 - Python Python implementation of Prim's, Kruskal's, union-find & d-Heaps
3	Graph Algorithms I Breadth-first search, queues Depth-first search, stacks Topological sorting Bipartite testing	AD Ch. 3 Lecture 5 - Jan 28 - Big-O notation Lecture 6 - Jan 30 - More Big-O notation MIT OCW Asympotitic Notation Lecture 7 - Feb 4 - BFS, DFS Lecture 8 - Feb 6 - Topological Sorting, Bipartite testing
4	Graph Algorithms II Shortest Path (Dijkstra's algorithm) Shortest Paths (Bellman Ford) Large-scale heuristic search with A*	AD 4.4, 6.8 Lecture 9 - Feb 8 - Dijkstra's algorithm: shortest paths in graphs Lecture 10 - Feb 11,13,15 - Handling negative weights in shortest paths Lecture 11 - Feb 18 - A*
5	Divide and Conquer Algorithms Big idea: Recursion & Self-similarity Finding closest pair of points Mergesort Counting inversions Solving Big-Oh recurrences	AD 5.1-5.2,5.4,5.6 Lecture 12 - Feb 20 - Counting inversions & divide and conquer Lecture 13 - Feb 22 - Finding closest pair of points
6	Dictionary Data Structures Splay trees	Lecture 14 - Mar 6 - Binary search trees & splay trees Splay Tree Demo 15-451 Splay Lecture Notes
7	Dynamic Programming Algorithms Big idea: parameterized self-similarity Subset Sum Sequence Alignment RNA Folding	AD 6.1-5 Lecture 15 - Mar 22 - Subset sum & Knapsack Lecture 16 - Edit distance & sequence alignment Alignment demo Lecture 17 - RNA Folding
8	More Dynamic Programming Algorithms Segmented least squares Matrix Multiplication Optimal Binary Search trees Local alignment	AD 6.6-6.8 Lecture 18 - More Dynamic Programming Examples Local alignment (only covered briefly) Other DP examples (not covered in class): Alignment with general gap penalties; Hidden Markov Models A demo of the gas station problem by David Farrow.
9	Network Flow Algorithms Big idea: Reduction Network flow and matching Using network flow to solve non-flow problems Circulation with demands	AD 7.1-7.3,7.5,7.7,7.8 Lecture 19 - Network flow Lecture 20 - Bipartite Matching Lecture 21 - Image Segmentation Lecture 22 - Extensions to network flow
10	Linear and Integer Programming Algorithms The linear and integer programming problems Network flows with weights and other extensions Other applications of linear and integer programming	AD 11.6 (brief discussion) Lecture 23 - Linear Programming GNU GLPK Software
11	NP-completeness Big idea: Turning proofs of impossibility into programming Theory of P, NP, NP-completeness, and the P=NP question Proof of NP-hardness of SAT, Coloring, TSP	AD 8.1-8.7 Lecture 24 - NP Hardness and Reductions Lecture 25 - More NP Reductions Beyond Computation: The P vs NP Problem - Michael Sipser A recent book review about P=NP question
12	Summary Summary	Lecture 26 - Summary

"AD" indicates sections in the Algorithm Design textbook.