Algorithms: Design and Analysis, Part 1

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Algorithms: Design and Analysis, Part 1

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Description

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About this course: Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists. Specific topics include: "Big-oh" notation, sorting and searching, divide and conquer (master method, integer and matrix multip…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists. Specific topics include: "Big-oh" notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), randomized algorithms (QuickSort, contraction algorithm for min cuts), data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of BFS and DFS, connectivity, shortest paths). 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.

Who is this class for: Learners with at least a little bit of programming experience who want to learn the essentials of algorithms. In a University computer science curriculum, this course is typically taken in the third year.

Created by:   Stanford University
  • Taught by:    Tim Roughgarden, Associate Professor

    Computer Science
Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.9 stars Average User Rating 4.9See all 56 reviews Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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About Stanford University The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Syllabus


WEEK 1


Week 1
Introduction; "big-oh" notation and asymptotic analysis; divide-and-conquer basics. 


18 videos, 4 readings expand
Graded: Problem Set #1

WEEK 2


Week 2
The master method for analyzing divide and conquer algorithms; the QuickSort algorithm and its analysis; probability review. 


15 videos, 2 readings expand
Graded: Problem Set #2

WEEK 3


Week 3
Linear-time selection; graphs, cuts, and the contraction algorithm. 


11 videos, 2 readings expand
Graded: Problem Set #3

WEEK 4


Week 4
Breadth-first and depth-first search; computing strong components; applications. 


9 videos, 2 readings expand
Graded: Problem Set #4

WEEK 5


Week 5
Dijkstra's shortest-path algorithms; heaps; balanced binary search trees. 


13 videos, 2 readings expand
Graded: Problem Set #5

WEEK 6


Week 6
Hashing; bloom filters. 


16 videos, 2 readings expand
Graded: Problem Set #6

WEEK 7


Week 7
Final exam (1 attempt per 24 hours) 

Graded: Final Exam
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