Date
Lecture
Topic
Reference
M 8/28
1
Introduction
[Er0]
W 8/30
2
Divide & Conquer I: Recursion and Mergesort
[DPV2,Er1]
W 9/6
3
Divide & Conquer II: Randomized Quicksort
[Er1, Ed2]
M 9/11
4
Divide & Conquer III: Selection
[DPV2,Er1]
W 9/13
5
Parallel Algorithms I: Concepts and Analysis
[CLRS27]
M 9/18
6
Parallel Algorithms II: Algorithm Design
[CLRS27]
W 9/20
7
Dynamic Programming I: Introduction,Sequence Alignment
[DPV6,Er3]
M 9/25
8
Dynamic Programming II: Knapsack, Chain Matrix
[DPV6,Er3]
W 9/27
9
Graph Traversal I: Depth-First Search, Topological Sort
[DPV3,Er6]
M 10/2
10
Graph Traversal II: Breadth-First Search
[DPV4,Er5]
W 10/4
11
Shortest Paths I: Dijkstra's Algorithm
[DPV4,Er8]
M 10/9
12
Shortest Paths II: A*, Bellman-Ford, Floyd Warshall
[DPV4,Er8,9]
W 10/11
13
Greedy I: Scheduling and Compression
W 10/18
14
Industry guest speaker(s)
[DPV5,Er4]
M 10/23
15
MIDTERM EXAM (Lectures 1-12)
W 10/25
16
Greedy II: Minimum Spanning Tree
[DPV5,Er7]
M 10/30
17
Cuts and Flows I: Max-flow and Min-cut
[Er10]
W 11/1
18
Cuts & Flows II: Computing flows
[Er10]
M 11/6
19
Hardness I: Complexity Classes and NP-Completeness
[DPV8,Er12]
W 11/8
20
Hardness II: NP-Complete Problems and Reductions
[DPV8,Er12]
M 11/13
21
Hardness III: More reductions and implications
[DPV8,Er12]
W 11/15
22
Approximation: Greedy scheduling, Traveling Salesperson
TBA
M 11/20
23
Big Data 1: Clustering-Greedy
[CLRS33,Ph8]
M 11/27
24
Big Data 2: Clustering-kmeans and randomized
[CLRS33,Ph8]
W 11/29
25
Big Data 3: Hashing
[ErApp5]
M 12/4
26
Big Data 4: Sketching
[CY3]
W 12/6
27
LATE-TERM EXAM (L14-25)
[CLRS27]
Th 12/14
Exam
FINAL EXAM 2-5 pm (All lectures)