À propos de ce cours : 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).
Les destinataires de ce cours : 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.
Créé par : Stanford University
Enseigné par : Tim Roughgarden, Professor
Computer Science
| Informations de base | Cours 4 de 4 dans Algorithms Specialization |
| Niveau | Intermediate |
| Engagement | 4 weeks of study, 4-8 hours/week |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
14 vidéos, 4 lectures
- Reading: Week 1 Overview
- Reading: Overview, Resources, and Policies
- Reading: Lecture Slides
- Vidéo: Single-Source Shortest Paths, Revisted
- Vidéo: Optimal Substructure
- Vidéo: The Basic Algorithm I
- Vidéo: The Basic Algorithm II
- Vidéo: Detecting Negative Cycles
- Vidéo: A Space Optimization
- Vidéo: Internet Routing I [Optional]
- Vidéo: Internet Routing II [Optional]
- Vidéo: Problem Definition
- Vidéo: Optimal Substructure
- Vidéo: The Floyd-Warshall Algorithm
- Vidéo: A Reweighting Technique
- Vidéo: Johnson's Algorithm I
- Vidéo: Johnson's Algorithm II
- Reading: Optional Theory Problems (Week 1)
Noté: Problem Set #1
Noté: Programming Assignment #1
SEMAINE 2
Week 2
NP-complete problems and exact algorithms for them.
11 vidéos, 2 lectures
- Reading: Week 2 Overview
- Vidéo: Polynomial-Time Solvable Problems
- Vidéo: Reductions and Completeness
- Vidéo: Definition and Interpretation of NP-Completeness I
- Vidéo: Definition and Interpretation of NP-Completeness II
- Vidéo: The P vs. NP Question
- Vidéo: Algorithmic Approaches to NP-Complete Problems
- Vidéo: The Vertex Cover Problem
- Vidéo: Smarter Search for Vertex Cover I
- Vidéo: Smarter Search for Vertex Cover II
- Vidéo: The Traveling Salesman Problem
- Vidéo: A Dynamic Programming Algorithm for TSP
- Reading: Optional Theory Problems (Week 2)
Noté: Problem Set #2
Noté: Programming Assignment #2
SEMAINE 3
Week 3
Approximation algorithms for NP-complete problems.
6 vidéos, 1 lecture
- Reading: Week 3 Overview
- Vidéo: A Greedy Knapsack Heuristic
- Vidéo: Analysis of a Greedy Knapsack Heuristic I
- Vidéo: Analysis of a Greedy Knapsack Heuristic II
- Vidéo: A Dynamic Programming Heuristic for Knapsack
- Vidéo: Knapsack via Dynamic Programming, Revisited
- Vidéo: Ananysis of Dynamic Programming Heuristic
Noté: Problem Set #3
Noté: Programming Assignment #3
SEMAINE 4
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
11 vidéos, 3 lectures
- Reading: Week 4 Overview
- Vidéo: The Maximum Cut Problem I
- Vidéo: The Maximum Cut Problem II
- Vidéo: Principles of Local Search I
- Vidéo: Principles of Local Search II
- Vidéo: The 2-SAT Problem
- Vidéo: Random Walks on a Line
- Vidéo: Analysis of Papadimitriou's Algorithm
- Vidéo: Stable Matching [Optional]
- Vidéo: Matchings, Flows, and Braess's Paradox [Optional]
- Vidéo: Linear Programming and Beyond [Optional]
- Vidéo: Epilogue
- Reading: Optional Theory Problems (Week 4)
- Reading: Info and FAQ for final exam
Noté: Problem Set #4
Noté: Programming Assignment #4
Noté: Final Exam
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
tough course
Thanks a lot. It is time consuming, need a lot of thinking and practising to finish the homework. And it is worth taking. After this, we can go deep into cs.
unlike previous 3 courses
some of course 4's quiz problems are really difficult and not sure how to approach...
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DK
nice challenging problems, good algo design practice