This recitation discusses the first problem from Problem Set 3, covering sweep-line algorithms and range queries.

Recitation 8: Simulation Algorithms

This recitation starts with a review of comparison sorting methods, and then discusses counting sort and radix sort.

Recitation 7: Comparison Sort, Counting and Radix Sort

Sorting is introduced, and motivated by problems that become easier once the inputs are sorted.  The lecture covers insertion sort, then discusses merge sort and analyzes its running time using a recursion tree.

Lecture 3: Insertion Sort, Merge Sort

This lecture describes an algorithm as a computational procedure to solve a problem, covers the random access machine and pointer models of computation, and introduces the document distance problem.

Lecture 2: Models of Computation, Document Distance

This lecture begins with a review of graphs and applications of graph search, discusses graph representations such as adjacency lists, and covers breadth-first search.

Lecture 13: Breadth-First Search (BFS)

This recitation covers the wood-cutting problem (dynamic programming) and Bloom filters (hashing, probability).

Recitation 24: Final Exam Review

This recitation discusses the knapsack problem and polynomial time vs. pseudo-polynomial time.

Recitation 21: Dynamic Programming: Knapsack Problem

This recitation reviews numerics and graphs in preparation for Quiz 2.

Recitation 18: Quiz 2 Review

This recitation looks at player positions in the Dance Dance Revolution game, along the lines of the guitar fingering example shown in lecture.

Recitation 22: Dynamic Programming: Dance Dance Revolution

This recitation uses dynamic programming to find subsequences in the card game Crazy Eights, and to find the shortest path in a graph.

Recitation 19: Dynamic Programming: Crazy Eights, Shortest Path

I could only see first 10 lectures in iTuneU for this course. Rest I had to access on youtube.
Erik &amp; Srini are amazing.  Please please upload rest of the lectures.

Complete Course is not uploaded in iTuneU!!

Lecture 2 is actually the video of lecture 3

Introduction to Algorithms (2011)

In this lecture, binary search trees are introduced, and several operations are covered: insertion, finding a value, finding the minimum element.

Lecture 5: Binary Search Trees, BST Sort

Lecture 6: AVL Trees, AVL Sort

This recitation covers the Python cost model and looks at the code for document distance, including main and most functions except count_frequency.

Recitation 2: Python Cost Model, Document Distance

This lecture starts with dictionaries in Python, considers the problems with using a direct-access table, and introduces hashing.  The lecture discusses hashing with chaining, which is one way of dealing with collisions.

Lecture 8: Hashing with Chaining

This lecture introduces dynamic programming, in which careful exhaustive search can be used to design polynomial-time algorithms.  The Fibonacci and shortest paths problems are used to introduce guessing, memoization, and reusing solutions to subproblems.

Lecture 19: Dynamic Programming I: Fibonacci, Shortest Paths

This lecture covers depth-first search, including edge classification, and how DFS is used for cycle detection and topological sort.

Lecture 14: Depth-First Search (DFS), Topological Sort

This recitation discusses the Rubik's cube problem from Problem Set 6, and then uses a graph model to find an optimal build order for a simplified version of the StarCraft game.

Recitation 16: Rubik's Cube, StarCraft Zero

This recitation revisits the perfect-information blackjack problem that was covered in lecture.

Recitation 20: Dynamic Programming: Blackjack

This recitation starts with a review of recursion trees and recurrences, and then discusses binary search trees.

Recitation 5: Recursion Trees, Binary Search Trees

This lecture shows how to find shortest paths in directed acyclic graphs (DAGs) using topological sort, and in graphs without negative edges using Dijkstra's algorithm.

Lecture 16: Dijkstra

This recitation continues to look at versions of the document distance code, and briefly discusses insertion and merge sort.

Recitation 3: Document Distance, Insertion and Merge Sort

This lecture covers table resizing, amortized analysis, string matching with the Karp-Rabin algorithm, and rolling hashes.

Lecture 9: Table Doubling, Karp-Rabin

This lecture starts with a five-step process for dynamic programming, and then covers text justification and perfect-information blackjack.  The lecture also describes how parent pointers are used to recover the solution.

Lecture 20: Dynamic Programming II: Text Justification, Blackjack

This recitation mostly covers rolling hashes, with a short discussion of amortized analysis at the end.

Recitation 9: Rolling Hashes, Amortized Analysis

This recitation covers insertion, deletion, and rebalancing of AVL trees.

Recitation 6: AVL Trees

This lecture reviews shortest path notation, considers a generic shortest path algorithm, and then describes and proves the Bellman-Ford algorithm, which can handle graphs with negative cycles.

Lecture 17: Bellman-Ford

This recitation discusses principles of algorithm design, using example problems from previous final exams.

Recitation 11: Principles of Algorithm Design

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