À propos de ce cours : This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Les destinataires de ce cours : While this course was originally designed for first- and second-year undergraduates, it is appropriate not only for college students but also for professionals. Two-thirds of our learners are college graduates working in industry.
Créé par : Princeton University
Enseigné par : Robert Sedgewick, William O. Baker *39 Professor of Computer Science
Computer Science
Enseigné par : Kevin Wayne, Senior Lecturer
Computer Science
| Niveau | Intermédiaire |
| Engagement | 6 weeks of study, 6–10 hours per week. |
| Langue | English, Sous-titres : Korean |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Introduction
Welcome to Algorithms, Part II.
1 vidéo, 2 lectures
- Reading: Welcome to Algorithms, Part II
- Reading: Lecture Slides
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
6 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: Graph API
- Vidéo: Depth-First Search
- Vidéo: Breadth-First Search
- Vidéo: Connected Components
- Vidéo: Graph Challenges
- Practice Quiz: Interview Questions: Undirected Graphs (ungraded)
Directed Graphs
In this lecture we study directed graphs. We begin with depth-first search and breadth-first search in digraphs and describe applications ranging from garbage collection to web crawling. Next, we introduce a depth-first search based algorithm for computing the topological order of an acyclic digraph. Finally, we implement the Kosaraju−Sharir algorithm for computing the strong components of a digraph.
5 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Digraph API
- Vidéo: Digraph Search
- Vidéo: Topological Sort
- Vidéo: Strong Components
- Practice Quiz: Interview Questions: Directed Graphs (ungraded)
Noté: WordNet
SEMAINE 2
Minimum Spanning Trees
In this lecture we study the minimum spanning tree problem. We begin by considering a generic greedy algorithm for the problem. Next, we consider and implement two classic algorithm for the problem—Kruskal's algorithm and Prim's algorithm. We conclude with some applications and open problems.
6 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: Greedy Algorithm
- Vidéo: Edge-Weighted Graph API
- Vidéo: Kruskal's Algorithm
- Vidéo: Prim's Algorithm
- Vidéo: MST Context
- Practice Quiz: Interview Questions: Minimum Spanning Trees (ungraded)
Shortest Paths
In this lecture we study shortest-paths problems. We begin by analyzing some basic properties of shortest paths and a generic algorithm for the problem. We introduce and analyze Dijkstra's algorithm for shortest-paths problems with nonnegative weights. Next, we consider an even faster algorithm for DAGs, which works even if the weights are negative. We conclude with the Bellman−Ford−Moore algorithm for edge-weighted digraphs with no negative cycles. We also consider applications ranging from content-aware fill to arbitrage.
5 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Shortest Path Properties
- Vidéo: Dijkstra's Algorithm
- Vidéo: Edge-Weighted DAGs
- Vidéo: Negative Weights
- Practice Quiz: Interview Questions: Shortest Paths (ungraded)
Noté: Seam Carving
SEMAINE 3
Maximum Flow and Minimum Cut
In this lecture we introduce the maximum flow and minimum cut problems. We begin with the Ford−Fulkerson algorithm. To analyze its correctness, we establish the maxflow−mincut theorem. Next, we consider an efficient implementation of the Ford−Fulkerson algorithm, using the shortest augmenting path rule. Finally, we consider applications, including bipartite matching and baseball elimination.
6 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: Ford–Fulkerson Algorithm
- Vidéo: Maxflow–Mincut Theorem
- Vidéo: Running Time Analysis
- Vidéo: Java Implementation
- Vidéo: Maxflow Applications
- Practice Quiz: Interview Questions: Maximum Flow (ungraded)
Noté: Baseball Elimination
Radix Sorts
In this lecture we consider specialized sorting algorithms for strings and related objects. We begin with a subroutine to sort integers in a small range. We then consider two classic radix sorting algorithms—LSD and MSD radix sorts. Next, we consider an especially efficient variant, which is a hybrid of MSD radix sort and quicksort known as 3-way radix quicksort. We conclude with suffix sorting and related applications.
6 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Key-Indexed Counting
- Vidéo: LSD Radix Sort
- Vidéo: MSD Radix Sort
- Vidéo: 3-way Radix Quicksort
- Vidéo: Suffix Arrays
- Practice Quiz: Interview Questions: Radix Sorts (ungraded)
SEMAINE 4
Tries
In this lecture we consider specialized algorithms for symbol tables with string keys. Our goal is a data structure that is as fast as hashing and even more flexible than binary search trees. We begin with multiway tries; next we consider ternary search tries. Finally, we consider character-based operations, including prefix match and longest prefix, and related applications.
3 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: Ternary Search Tries
- Vidéo: Character-Based Operations
- Practice Quiz: Interview Questions: Tries (ungraded)
Substring Search
In this lecture we consider algorithms for searching for a substring in a piece of text. We begin with a brute-force algorithm, whose running time is quadratic in the worst case. Next, we consider the ingenious Knuth−Morris−Pratt algorithm whose running time is guaranteed to be linear in the worst case. Then, we introduce the Boyer−Moore algorithm, whose running time is sublinear on typical inputs. Finally, we consider the Rabin−Karp fingerprint algorithm, which uses hashing in a clever way to solve the substring search and related problems.
5 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Brute-Force Substring Search
- Vidéo: Knuth–Morris–Pratt
- Vidéo: Boyer–Moore
- Vidéo: Rabin–Karp
- Practice Quiz: Interview Questions: Substring Search (ungraded)
Noté: Boggle
SEMAINE 5
Regular Expressions
A regular expression is a method for specifying a set of strings. Our topic for this lecture is the famous grep algorithm that determines whether a given text contains any substring from the set. We examine an efficient implementation that makes use of our digraph reachability implementation from Week 1.
5 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: REs and NFAs
- Vidéo: NFA Simulation
- Vidéo: NFA Construction
- Vidéo: Regular Expression Applications
- Practice Quiz: Interview Questions: Regular Expressions (ungraded)
Data Compression
We study and implement several classic data compression schemes, including run-length coding, Huffman compression, and LZW compression. We develop efficient implementations from first principles using a Java library for manipulating binary data that we developed for this purpose, based on priority queue and symbol table implementations from earlier lectures.
4 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Run-Length Coding
- Vidéo: Huffman Compression
- Vidéo: LZW Compression
- Practice Quiz: Interview Questions: Data Compression (ungraded)
Noté: Burrows–Wheeler
SEMAINE 6
Reductions
Our lectures this week are centered on the idea of problem-solving models like maxflow and shortest path, where a new problem can be formulated as an instance of one of those problems, and then solved with a classic and efficient algorithm. To complete the course, we describe the classic unsolved problem from theoretical computer science that is centered on the concept of algorithm efficiency and guides us in the search for efficient solutions to difficult problems.
4 vidéos, 2 lectures, 1 quiz pour s'exercer
- Reading: Overview
- Reading: Lecture Slides
- Vidéo: Designing Algorithms
- Vidéo: Establishing Lower Bounds
- Vidéo: Classifying Problems
- Practice Quiz: Interview Questions: Reductions (ungraded)
Linear Programming (optional)
The quintessential problem-solving model is known as linear programming, and the simplex method for solving it is one of the most widely used algorithms. In this lecture, we given an overview of this central topic in operations research and describe its relationship to algorithms that we have considered.
4 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Simplex Algorithm
- Vidéo: Simplex Implementations
- Vidéo: Linear Programming Reductions
- Practice Quiz: Interview Questions: Linear Programming (ungraded)
Intractability
Is there a universal problem-solving model to which all problems that we would like to solve reduce and for which we know an efficient algorithm? You may be surprised to learn that we do no know the answer to this question. In this lecture we introduce the complexity classes P, NP, and NP-complete, pose the famous P = NP question, and consider implications in the context of algorithms that we have treated in this course.
6 vidéos, 1 lecture, 1 quiz pour s'exercer
- Reading: Lecture Slides
- Vidéo: Search Problems
- Vidéo: P vs. NP
- Vidéo: Classifying Problems
- Vidéo: NP-Completeness
- Vidéo: Coping with Intractability
- Practice Quiz: Interview Questions: Intractability (ungraded)
FAQ
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Créateurs
Princeton University
Princeton University is a private research university located in Princeton, New Jersey, United States. It is one of the eight universities of the Ivy League, and one of the nine Colonial Colleges founded before the American Revolution.
Notation et examens
I LOVE IT!
This class is amazing. If you follow this course schedule strictly, and finish the homework and interview questions, you are gonna to learn a lot of algorithm thinking methods.
Great course on algorithms and require some in-depth thinking to get all the assignments right!
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W,
One of the parts I like for this course is the assignment. The assignment is to implement the algorithm which is useful in practice. Thinking how to implement each and running each after implementation are entertaining and make me think about the algorithm deeply. An introduction to the various algorithms for different type of problems broaden my horizons.
Thanks for providing this wonderful course.