Coursera / Stanford University - Design and Analysis of Algorithms I : Algorithms and Data Structures

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Coursera / Stanford University - Design and Analysis of Algorithms I : Algorithms and Data Structures (Size: 768.39 MB)
 4 Additional Examples [Review - Optional] (8 min).webm6.23 MB
 3 Basic Examples (7 min).webm5.85 MB
 2 Basic Examples (7 min).webm5.65 MB
 1 Big-Oh Notation (4 min).webm3.14 MB
 4 O(n log n) Algorithm for Closest Pair I [Advanced - Optional] (32 min).webm23.76 MB
 3 Strassen's Subcubic Matrix Multiplication Algorithm (22 min).webm16.18 MB
 5 O(n log n) Algorithm for Closest Pair II [Advanced - Optional] (19 min).webm14.92 MB
 2 O(n log n) Algorithm for Counting Inversions II (17 min).webm14.41 MB
 1 O(n log n) Algorithm for Counting Inversions I (13 min).webm10 MB
 6 Proof II (16 min).webm12.38 MB
 3 Examples (13 min).webm10.44 MB
 5 Interpretation of the 3 Cases (11 min).webm10.11 MB
 4 Proof I (10 min).webm9.34 MB
 2 Formal Statement (10 min).webm8.95 MB
 1 Motivation (8 min).webm6.59 MB
 4 Analysis of Contraction Algorithm (30 min).webm22.2 MB
 1 Graphs and Minimum Cuts (16 min).webm11.44 MB
 2 Graph Representations (14 min).webm10.54 MB
 3 Random Contraction Algorithm (9 min).webm6.24 MB
 5 Counting Minimum Cuts [Advanced - Optional] (7 min).webm5.46 MB
 2 Partitioning Around a Pivot (25 min).webm17.9 MB
 4 Choosing a Good Pivot (22min).webm15.72 MB
 3 Correctness of Quicksort [Review - Optional] (11 min).webm8.95 MB
 1 Quicksort- Overview (12 min).webm6.37 MB
 1 Analysis I- A Decomposition Principle [Advanced - Optional] (22 min).webm15.79 MB
 2 Analysis II- The Key Insight [Advanced - Optional] (12min).webm8.58 MB
 3 Analysis III- Final Calculations [Advanced - Optional] (9min).webm6.24 MB
 1 Part I [Review - Optional] (25 min).webm18.81 MB
 2 Part II [Review - Optional] (17 min).webm12.88 MB
 1 Randomized Selection - Algorithm (22 min).webm16.34 MB
 4 Deterministic Selection - Analysis I [Advanced - Optional] (22 min).webm16.32 MB
 2 Randomized Selection - Analysis (21 min).webm14.33 MB
 3 Deterministic Selection - Algorithm [Advanced - Optional] (17 min).webm13.38 MB
 6 Omega(n log n) Lower Bound for Comparison-Based Sorting [Advanced - Optional] (13 min).webm9.8 MB
 5 Deterministic Selection - Analysis II [Advanced - Optional] (13 min).webm9.54 MB
 7 Computing Strong Components- The Algorithm (29 min).webm22.21 MB
 8 Computing Strong Components- The Analysis (26 min).webm18.94 MB
 1 Graph Search - Overview (23 min).webm16.42 MB
 6 Topological Sort (22 min).webm15.57 MB
 9 Structure of the Web [Optional] (19 min).webm13.4 MB
 4 BFS and Undirected Connectivity (13 min).webm10.59 MB
 2 Breadth-First Search (BFS)- The Basics (14 min).webm10.43 MB
 3 BFS and Shortest Paths (8 min).webm5.48 MB
 5 Depth-First Search (DFS) The Basics (7 min).webm5.39 MB
 4 Dijkstra's Algorithm- Implementation and Running Time (26 min).webm20.3 MB
 1 Dijkstra's Shortest-Path Algorithm (21 min).webm15.11 MB
 3 Correctness of Dijkstra's Algorithm [Advanced - Optional] (19 min).webm14.21 MB
 2 Dijkstra's Algorithm- Examples (13 min).webm9.15 MB
 8 Universal Hashing- The Mathematics, Part I [Optional] (27 min).webm19.03 MB
 6 Hash Tables- Implementation Details, Part I (19 min).webm16.78 MB
 7 Universal Hashing- Motivation [Optional] (22 min).webm16.7 MB
 3 Heaps- Implementation Details [Advanced - Optional] (21 min).webm15.84 MB
 4 Hash Tables- Operations and Applications (19 min).webm14.94 MB
 5 Hash Tables- Implementation Details, Part I (19 min).webm13.86 MB
 2 Heaps- Operations and Applications (18 min).webm13.64 MB
 1 Data Structures- Overview (5 min).webm3.42 MB
 6 Guiding Principles for Analysis of Algorithms (15 min).webm13.79 MB
 1 Introduction Why Study Algorithms (19 min).webm13.77 MB
 4 Merge Sort Pseudocode (13 min).webm13.36 MB
 2 About the Course (17 min).webm12.07 MB
 5 Merge Sort Analysis (9 min).webm10.25 MB
 3 Merge Sort Motivation and Example (9 min).webm8.96 MB

Description

In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?

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Coursera / Stanford University - Design and Analysis of Algorithms I : Algorithms and Data Structures