About this course: 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).
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, Professor
Computer Science
| Basic Info | Course 3 of 4 in the Algorithms Specialization |
| Level | Intermediate |
| Commitment | 4 weeks of study, 4-8 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
16 videos, 4 readings
- Материал для самостоятельного изучения: Week 1 Overview
- Материал для самостоятельного изучения: Overview, Resources, and Policies
- Материал для самостоятельного изучения: Lecture slides
- Video: Application: Internet Routing
- Video: Application: Sequence Alignment
- Video: Introduction to Greedy Algorithms
- Video: Application: Optimal Caching
- Video: Problem Definition
- Video: A Greedy Algorithm
- Video: Correctness Proof - Part I
- Video: Correctness Proof - Part II
- Video: Handling Ties [Advanced - Optional]
- Video: MST Problem Definition
- Video: Prim's MST Algorithm
- Video: Correctness Proof I
- Video: Correctness Proof II
- Video: Proof of Cut Property [Advanced - Optional]
- Video: Fast Implementation I
- Video: Fast Implementation II
- Материал для самостоятельного изучения: Optional Theory Problems (Week 1)
Graded: Problem Set #1
Graded: Programming Assignment #1
WEEK 2
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
16 videos, 2 readings
- Материал для самостоятельного изучения: Week 2 Overview
- Video: Kruskal's MST Algorithm
- Video: Correctness of Kruskal's Algorithm
- Video: Implementing Kruskal's Algorithm via Union-Find I
- Video: Implementing Kruskal's Algorithm via Union-Find II
- Video: MSTs: State-of-the-Art and Open Questions [Advanced - Optional]
- Video: Application to Clustering
- Video: Correctness of Clustering Algorithm
- Video: Lazy Unions [Advanced - Optional]
- Video: Union-by-Rank [Advanced - Optional]
- Video: Analysis of Union-by-Rank [Advanced - Optional]
- Video: Path Compression [Advanced - Optional]
- Video: Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]
- Video: Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]
- Video: The Ackermann Function [Advanced - Optional]
- Video: Path Compression: Tarjan's Analysis I [Advanced - Optional]
- Video: Path Compression: Tarjan's Analysis II [Advanced - Optional]
- Материал для самостоятельного изучения: Optional Theory Problems (Week 2)
Graded: Problem Set #2
Graded: Programming Assignment #2
WEEK 3
Week 3
Huffman codes; introduction to dynamic programming.
11 videos, 1 reading
- Материал для самостоятельного изучения: Week 3 Overview
- Video: Introduction and Motivation
- Video: Problem Definition
- Video: A Greedy Algorithm
- Video: A More Complex Example
- Video: Correctness Proof I
- Video: Correctness Proof II
- Video: Introduction: Weighted Independent Sets in Path Graphs
- Video: WIS in Path Graphs: Optimal Substructure
- Video: WIS in Path Graphs: A Linear-Time Algorithm
- Video: WIS in Path Graphs: A Reconstruction Algorithm
- Video: Principles of Dynamic Programming
Graded: Problem Set #3
Graded: Programming Assignment #3
WEEK 4
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
10 videos, 3 readings
- Материал для самостоятельного изучения: Week 4 Overview
- Video: The Knapsack Problem
- Video: A Dynamic Programming Algorithm
- Video: Example [Review - Optional]
- Video: Optimal Substructure
- Video: A Dynamic Programming Algorithm
- Video: Problem Definition
- Video: Optimal Substructure
- Video: Proof of Optimal Substructure
- Video: A Dynamic Programming Algorithm I
- Video: A Dynamic Programming Algorithm II
- Материал для самостоятельного изучения: Optional Theory Problems (Week 4)
- Материал для самостоятельного изучения: Info and FAQ for final exam
Graded: Problem Set #4
Graded: Programming Assignment #4
Graded: Final Exam
FAQs
How It Works
Задания курса
Каждый курс — это интерактивный учебник, который содержит видеоматериалы, тесты и проекты.
Помощь сокурсников
Общайтесь с тысячами других учащихся: обсуждайте идеи, материалы курса и помогайте друг другу осваивать новые понятия.
Сертификаты
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Creators
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.
Pricing
| Прослушать | Приобрести курс | |
|---|---|---|
| Получить доступ к материалам курса | Available | Available |
| Получить доступ к оцениваемым материалам курса | Not available | Available |
| Получить итоговую оценку | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Excellent lectures on Dynamic Programming! The examples chosen in lectures are really typical! I tried to practice on more DP problems on Geeksforgeeks. Surprisingly many DP problems are just extension of the examples gone through in these videos. Reading the CLRS book after watching the lectures become much much easier!
Very good course material with practical implementation.
It was a great course, I really want to thank Tim for another great work.
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JW
Still enjoying it -- looking forward to course 4!