6.006 | Spring 2008 | Undergraduate
Introduction to Algorithms

## Calendar

This table provides information about both the lecture (L) and recitation (R) sessions.

SES # TOPICS KEY DATES
Introduction and document distance
L1 Introduction and document distance

R1 Document distance in Python (docdist{1,2,3,4}.py)

L2 More document distance, mergesort

R2 Python cost model, review for asymptotic notation and mergesort

Binary search trees
L3 Airplane scheduling, binary search trees

R3 Binary search trees

L4 Balanced binary search trees

R4 AVL Trees (balanced binary search trees)

Hashing
L5 Hashing I: chaining, hash functions Problem set 1 due
R5 Hashing in Python, mutability

L6 Hashing II: table doubling, Karp-Rabin

R6 Karp-Rabin review, rolling hashes principles and code

L7 Hashing III: open addressing

R7

Open addressing: theory review, Python code

More rolling hashes

Sorting
L8 Sorting I: heaps

R8

Overview of sorting methods

Heaps as data structures: principles, sorting, priority queues

L9 Sorting II: heaps Problem set 2 due
L10 Sorting III: lower bounds, linear-time sorting

R9 Quiz review: interesting problems Quiz 1
L11 Sorting IV: stable sorting, radix sort

R10 Counting, radix and bucket sorting, gas simulation

Searching
L12 Searching I: graph search, representations, and applications

L13 Searching II: breadth-first search and depth-first search Problem set 3 due
L14 Searching III: topological sort and NP-completeness

R11 Breadth-first search and depth-first search

Shortest paths
L15 Shortest paths I: intro

R12 Assistance for problem set

L16 Shortest paths II: Bellman-Ford Problem set 4 due
R13

Generic shortest-path algorithm: concepts, properties

Bellman-Ford: examples, negative-cost cycles

L17 Shortest paths III: Dijkstra

R14

Hands-on Dijkstra: pseudocode, preconditions, examples, why it works

Priority queues: review, extended Python implementation

L18 Shortest paths IV: Dijkstra speedups

R15 Array implementation of Dijkstra Quiz 2
Dynamic programming
L19 Dynamic programming I: memoization, Fibonacci, Crazy Eights, guessing

R16 Hands-on dynamic programming: big ideas, memoization in Fibonacci, crazy cards, Dijkstra and Bellman-Ford algorithm as dynamic programming

L20 Dynamic programming II: longest common subsequence (LCS), parent pointers

R17 More dynamic programming: beating Super Mario Brothers, getting points back on tests (LCS), Crazy Eights

L21 Dynamic programming III: text justification, parenthesization, knapsack, pseudopolynomial time, Tetris training Problem set 5 due
R18 Even more dynamic programming: maximum-sum sub-array, more Tetris

L22 Dynamic programming IV: piano fingering, structural DP (trees), vertex cover, dominating set, and beyond

R19 Knapsack and its variants, structural dynamic programming: covering a tree with templates, dominating set

Numerics
L23 Numerics I

R20 Dynamic programming practice: live Python coding, dominating sets, structural dynamic programming: covering a tree with templates

L24 Numerics II Problem set 6 due
R21 Divide and conquer vs. dynamic programming on problems: matrix multiplication, tower, max-sum subarray, closest pair

R22 Numerics review, Strassen’s algorithm for matrix multiplication

Beyond 6.006
L25 Beyond 6.006: follow-on classes, geometric folding algorithms

R23 Review for the final exam