Lecture Schedule

This page is a lecture view of the overall course schedule.

Num	Date	Topic	References
L01	Th 1/8	Algorithms, asymptotic analysis	[Er0]
L02	Tu 1/13	Divide & Conquer I: Merge Sort and Recurrences	[DPV2,Er1]
L03	Th 1/15	Divide & Conquer II: Quicksort and Randomization	[Er1,Ed2]
L04	Tu 1/20	Parallel Algorithms I: Concepts and Analysis	[CLRS27]
L05	Th 1/22	Parallel Algorithms II: Algorithm Design	[CLRS27]
L06	Tu 1/27	Dynamic Programming I: Introduction, memoization	[DPV6,Er3]
L07	Th 1/29	Dynamic Programming II: Sequence Alignment, Dependency Graph	[DPV6,Er3]
L08	Tu 2/3	Dynamic Programming III: Matrix Multiplication, Trees	[DPV6,Er3]
L09	Th 2/5	Graph Traversal I: Depth-First Search, Connected Components	[DPV3,Er6]
L10	Tu 2/10	Graph Traversal II: Topological Sort, Strongly Connectivity	[DPV3,Er6]
L11	Th 2/12	Graph Traversal III: Breadth First Search	[DPV4,Er5]
L12	Tu 2/17	Shortest Paths I: Dijkstra's Algorithm, A*	[DPV,Er8]
L13	Th 2/19	Shortest Paths II: Dynamic Programming	[DPV4,Er9]
E01	Tu 2/24	EXAM I (L1-L12)
L14	Th 2/26	Greedy I: Minimum Spanning Tree	[DPV4,Er7]
L15	Tu 3/3	Greedy II: Compression, scheduling	[DPV5,Er4]
L16	Th 3/5	Cuts and Flows I: Max-flow and Min-cut	[Er10]
	Tu 3/10	NO LECTURE (spring recess)
	Th 3/12	NO LECTURE (spring recess)
L17	Tu 3/17	Cuts and Flows II: Algorithm, applications	[Er10,11]
L18	Th 3/19	Hardness I: Complexity Classes and NP-Completeness	[Er12]
L19	Tu 3/24	Hardness II: NP-Complete Problems and Reductions	[DPV8,Er12]
L20	Th 3/26	Hardness III: Approximation algorithms	[DPV8,Er12]
L21	Tu 3/31	Big Data 1: Clustering-Greedy	[Ph]
L22	Th 4/2	Big Data 2: Clustering-kmeans and randomized	[CLRS33,Ph8]
L23	Tu 4/7	Big Data 3: Hashing	[CLRS33,Ph8]
L24	Th 4/9	Big Data 4: Sketching	[Ph??]
E02	Tu 4/14	EXAM II (L13-L24)
	Wed 4/29	FINAL EXAM (2:00pm-5:0pm, cumulative)