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- THE BELLMAN-FORD ALGORITHM- Detecting Negative Cycles
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课时1：Why Study Algorithms
课时2：Integer Multiplication
课时3：Karatsuba Multiplication
课时4：Merge Sort Motivation and Example
课时5：Merge Sort Pseudocode
课时6：Merge Sort Analysis
课时7：Guiding Principles for Analysis of Algorithms
课时8：The Gist
课时9：Big Oh Notation
课时10：Basic Examples
课时11：Big Omega and Theta
课时12：Additional Examples Review Optional
课时13：On log n Algorithm for Counting Inversions I
课时14：On log n Algorithm for Counting Inversions II
课时15：Strassen 's Subcubic Matrix Multiplication Algorithm
课时16：On log n Algorithm for Closest Pair I Advanced Optional
课时17：On log n Algorithm for Closest Pair II Advanced Optional
课时18：Motivation
课时19：Formal Statement
课时20：Examples
课时21：Proof I
课时22：Interpretation of the 3 Cases
课时23：Proof II
课时24：Quicksort Overview
课时25：Partitioning Around a Pivot
课时26：Choosing a Good Pivot
课时27：Analysis I A Decomposition Principle Advanced Optional
课时28：Analysis II The Key Insight Advanced Optional
课时29：Analysis III Final Calculations Advanced Optional
课时30：Omegan log n Lower Bound for Comparison Based Sorting Advanced Optional
课时31：Randomized Selection Algorithm
课时32：Randomized Selection Analysis
课时33：Deterministic Selection Algorithm Advanced Optional
课时34：Deterministic Selection Analysis I Advanced Optional
课时35：Deterministic Selection Analysis II Advanced Optional
课时36：Correctness of Quicksort Review Optional
课时37：Part I Review Optional
课时38：Graphs and Minimum Cuts
课时39：Graph Representations
课时40：Graph Search Overview
课时41：Breadth First Search BFS The Basics
课时42：BFS and Shortest Paths
课时43：BFS and Undirected Connectivity
课时44：Depth First Search DFS The Basics
课时45：Topological Sort
课时46：Computing Strong Components The Algorithm
课时47：Computing Strong Components The Analysis
课时48：Structure of the Web Optional
课时49：Dijkstra 's Shortest Path Algorithm
课时50：Dijkstra 's Algorithm Examples
课时51：Correctness of Dijkstra 's Algorithm Advanced Optional
课时52：Dijkstra 's Algorithm Implementation and Running Time
课时53：Data Structures Overview
课时54：Heaps Operations and Applications
课时55：Heaps Implementation Details Advanced Optional
课时56：Balanced Search Trees Operations and Applications
课时57：Binary Search Tree Basics, Part I
课时58：Binary Search Tree Basics, Part II
课时59：Rotations Advanced Optional
课时60：Hash Tables Operations and Applications
课时61：Hash Tables Implementation Details, Part I
课时62：Hash Tables Implementation Details, Part II
课时63：Pathological Data Sets and Universal Hashing Motivation
课时64：Bloom Filters The Basics
课时65：Bloom Filters Heuristic Analysis
课时66：INTRODUCTION TO GREEDY ALGORITHMS- Greedy Algorithms
课时67：A SCHEDULING APPLICATION- Problem Definition
课时68：A SCHEDULING APPLICATION- Greedy Algorithm
课时69：A SCHEDULING APPLICATION- Correctness Proof - Part I
课时70：A SCHEDULING APPLICATION- Correctness Proof - Part II
课时71：A SCHEDULING APPLICATION- Handling Ties
课时72：HUFFMAN CODES- Introduction and Motivation
课时73：HUFFMAN CODES- Problem Definition
课时74：HUFFMAN CODES- A Greedy Algorithm
课时75：HUFFMAN CODES- A More Complex Example
课时76：HUFFMAN CODES- Correctness Proof 1
课时77：HUFFMAN CODES- Correctness Proof 2
课时78：PRIM'S MINIMUM SPANNING TREE ALGORITHM- MST Problem Definition
课时79：PRIM'S MINIMUM SPANNING TREE ALGORITHM- Prims MST Algorithm
课时80：PRIM'S MINIMUM SPANNING TREE ALGORITHM- Fast Implementation 1
课时81：PRIM'S MINIMUM SPANNING TREE ALGORITHM- Fast Implementation 2
课时82：PRIM'S MINIMUM SPANNING TREE ALGORITHM- Correctness Proof 1
课时83：PRIM'S MINIMUM SPANNING TREE ALGORITHM- Correctness Proof 2
课时84：KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Kruskals MST Algorithm
课时85：KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Implementing Kruskals Algorithm via Union Find
课时86：KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Implementing Kruskals Algorithm via Union Find 2
课时87：ADVANCED UNION-FIND- Lazy Unions
课时88：KRUSKAL'S MINIMUM SPANNING TREE ALGORITHM- Correctness of Kruskals Algorithm
课时89：CLUSTERING- Application to Clustering
课时90：INTRODUCTION TO DYNAMIC PROGRAMMING- Introduction - Weighted Independent Sets in Path Graphs
课时91：INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - Optimal Substructure
课时92：INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - A Linear Time Algorithm
课时93：INTRODUCTION TO DYNAMIC PROGRAMMING- WIS in Path Graphs - A Reconstruction Algorithm
课时94：INTRODUCTION TO DYNAMIC PROGRAMMING- Principles of Dynamic Programming
课时95：THE KNAPSACK PROBLEM- The Knapsack Problem
课时96：THE KNAPSACK PROBLEM- A Dynamic Programming Algorithm
课时97：THE KNAPSACK PROBLEM- Example
课时98：SEQUENCE ALIGNMENT- Optimal Substructure
课时99：SEQUENCE ALIGNMENT- A Dynamic Programming Algorithm
课时100：OPTIMAL BINARY SEARCH TREES- Problem Definition
课时101：OPTIMAL BINARY SEARCH TREES- Optimal Substructure
课时102：OPTIMAL BINARY SEARCH TREES- Proof of Optimal Substructure
课时103：OPTIMAL BINARY SEARCH TREES- A Dynamic Programming Algorithm 1
课时104：OPTIMAL BINARY SEARCH TREES- A Dynamic Programming Algorithm 2
课时105：THE BELLMAN-FORD ALGORITHM- Single-Source Shortest Paths, Revisited
课时106：THE BELLMAN-FORD ALGORITHM- Optimal Substructure
课时107：THE BELLMAN-FORD ALGORITHM- The Basic Algorithm 1
课时108：THE BELLMAN-FORD ALGORITHM- The Basic Algorithm 2
课时109：THE BELLMAN-FORD ALGORITHM- Detecting Negative Cycles
课时110：ALL-PAIRS SHORTEST PATHS- Problem Definition
课时111：ALL-PAIRS SHORTEST PATHS- Optimal Substructure
课时112：ALL-PAIRS SHORTEST PATHS- The Floyd Warshall Algorithm
课时113：A Field Guide to Algorithm Design (Epilogue to the Algorithms Illuminated book series)
课时114：Overview and Prerequisites
课时115：The Algorithmic Mystery of MST vs. TSP
课时116：Possible Levels of Expertise
课时117：Easy and Hard Problems
课时118：Algorithmic Strategies for NP-Hard Problems
课时119：A Simple Recipe for Proving NP-Hardness
课时120：Rookie Mistakes
课时121：Makespan Minimization Part 1
课时122：Makespan Minimization Part 2
课时123：A Greedy Heuristic for Maximum Coverage Part 1
课时124：A Greedy Heuristic for Maximum Coverage Part 2
课时125：A Greedy Heuristic for Influence Maximization 1
课时126：A Greedy Heuristic for Influence Maximization 2
课时127：The 2-OPT Heuristic for the TSP Part 1
课时128：The 2-OPT Heuristic for the TSP Part 2
课时129：Principles of Local Search Part 1
课时130：Principles of Local Search Part 2
课时131：The Bellman-Held-Karp Algorithm for TSP Part 1
课时132：The Bellman-Held-Karp Algorithm for TSP Part 2
课时133：Color Coding Part 1
课时134：Color Coding Part 2
课时135：Problem-Specific Algorithms vs. Magic Boxes
课时136：Mixed Integer Programming Solvers
课时137：Satisfiability Solvers
课时138：Reductions Revisited
课时139：SAT and the Cook-Levin Theorem
课时140：The Big Picture
课时141：Independent Set Is NP-Hard
课时142：Directed Hamiltonian Path Is NP-Hard
课时143：The TSP Is NP-Hard
课时144：Subset Sum Is NP-Hard
课时145：Amassing Evidence of Intractability
课时146：Decision, Search, and Optimization
课时147：NP- Problems with Easily Recognized Solutions
课时148：The P!=NP Conjecture
课时149：The Exponential Time Hypothesis
课时150：NP-Completeness
课时151：Repurposing Wireless Spectrum
课时152：Greedy Heuristics for Buying Back Licenses Pt 1
课时153：Greedy Heuristics for Buying Back Licenses Pt 2
课时154：Feasibility Checking Pt 1
课时155：Feasibility Checking Pt 2
课时156：Implementation as a Descending Clock Auction
课时157：The Final Outcome
课时158：A Field Guide to Algorithm Design (Epilogue to the Algorithms Illuminated book series)
课程介绍共计158课时，1天14小时39分27秒
算法是计算机科学的核心与灵魂。算法的应用范围极广，网络路由、计算基因组学、公钥加密学和数据库系统等的实现都需要算法。研究算法可以帮助我们成为更优秀的程序员，可以让我们具有更缜密的思维，并成功应对各种场合的技术面试。
这是一本非常容易上手的算法入门图书，它可作为程序员的学习用书，也适合想要学习算法和想提升算法思维能力的读者阅读。
本书主要包括以下内容：
渐进性分析；
大O表示法；
主方法；
快速分治算法；
随机化算法；
排序算法；
选择算法。
算法是计算机科学的核心与灵魂。算法的应用范围极广，网络路由、计算基因组学、公钥加密学和数据库系统等的实现都需要算法。研究算法可以帮助我们成为更优秀的程序员，可以让我们具有更缜密的思维，并成功应对各种场合的技术面试。
这是一本非常容易上手的算法入门图书，它可作为程序员的学习用书，也适合想要学习算法和想提升算法思维能力的读者阅读。
本书主要包括以下内容：
图的搜索和应用；
散列表；
最短路径算法；
布隆过滤器；
随机化算法；
堆；
搜索树。
