zed1025 / algorithms

Python implementations of Coursera Algorithms Specialization by Stanford University

Coursera Algorithms Specialization by Stanford

Course Review

• This course assumes you have the basic knowlege in algorithms and data structures and also familiar with one of the programming languages.
• Good lectures for preparing for the technical interviews for Software Engineers, Data Scientists and Machine Learning Engineers.
• Covers all the fundamental algorithms including divide and conquer, randomized algorithms, greedy algorithm and dynamic programming. Covers most of the advanced data structures including graph, binary trees, heaps, hashtables, union and find. Lacks the introduction of basic data structures such as arrays, strings, linked lists.
• Focuses more on the graph problems, such as shortest path and graph search using BFS and DFS.
• Introduces big-Oh notation (very important in technical interviews) and NP-complete problems. A lot of proofs on the correctness and running time of the algorithm.

Requirement

• Python 3

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

• Karatuba Multiplication
• Merge Sort
• Finding Closet Pair
• Counting Inversions
• Quick Sort
• Quick Select
• Karger MinCut (Randomized Contraction)

Graph Search, Shortest Paths, and Data Structures

• Korasaju's algorithm to compute strongly connected components
• Dijkstra's algorithm (single source shortest path)
• Finding medians using heaps
• 2Sum using Hashtable
• Binary search tree
• BFS on graph
• GFS on graph

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

• Job scheduling
• Prim's algorithm on MST
• Prim's algorithm on MST (using heap)
• Maximum spacing of k-clustering (using union find)
• Clustering using Hamming distance
• Knapsack problem
• Optimal BST
• Sequence alignment

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

• Bellman-Ford algorithm (single source shortest path
• Floyd-Warshall algorithm (all-pair shortest path
• Traveling Salesman Problem (dynamic programming)
• Travleling Sales Problem (heuristic approach)
• Papadimitriou's 2-SAT Algorithm