Algorithms: Design and Analysis, Part 2

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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 have a greater mastery of algorithms than almost anyone without a graduate degree in the subject. Specific topics in Part 2 include: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes), dynamic programming (knapsack, sequence alignme…

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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 have a greater mastery of algorithms than almost anyone without a graduate degree in the subject. Specific topics in Part 2 include: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes), dynamic programming (knapsack, sequence alignment, optimal search trees, shortest paths), NP-completeness and what it means for the algorithm designer, analysis of heuristics, local search. 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 10 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
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm. 


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

WEEK 2


Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional); Huffman codes. 


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

WEEK 3


Week 3
Dynamic programming: introduction, the knapsack problem, sequence alignment, and optimal binary search trees. 


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

WEEK 4


Week 4
The Bellman-Ford algorithm; all-pairs shortest paths. 


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

WEEK 5


Week 5
NP-complete problems and exact algorithms for them. 


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

WEEK 6


Week 6
Approximation and local search algorithms for NP-complete problems; the wider world of algorithms. 


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

WEEK 7


Final Exam
Final exam (1 attempt per 24 hours) 


1 reading expand
Graded: Final Exam
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