https://github.com/camilamaia/algorithms-stanford
[WIP] My source code for Learn To Think Like A Computer Scientist specialization
https://github.com/camilamaia/algorithms-stanford
algorithms course-work coursera ruby stanford
Last synced: about 1 month ago
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[WIP] My source code for Learn To Think Like A Computer Scientist specialization
- Host: GitHub
- URL: https://github.com/camilamaia/algorithms-stanford
- Owner: camilamaia
- Created: 2017-04-12T13:01:15.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-17T11:57:34.000Z (over 7 years ago)
- Last Synced: 2025-02-02T13:26:04.524Z (3 months ago)
- Topics: algorithms, course-work, coursera, ruby, stanford
- Language: Ruby
- Homepage: https://www.coursera.org/specializations/algorithms
- Size: 2.93 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stanford Specialization: Algorithms
My source code for [Learn To Think Like A Computer Scientist](https://www.coursera.org/specializations/algorithms) Coursera specialization - created by **Stanford University**
### About This Specialization
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
### About the instructor
Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
### Courses
#### Course 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
#### Course 2: Graph Search, Shortest Paths, and Data Structures
The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
#### Course 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
#### Course 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).