This course builds on the concepts introduced in Algorithms, Part I and provides an in-depth exploration of advanced algorithms and data structures.
The course is divided into six modules, covering topics such as graph processing, shortest paths, maximum flow, string processing, and data compression. Each module consists of several video lectures, quizzes, programming assignments, and an optional textbook.
In the graph processing module, students learn about graph representation, depth-first search, breadth-first search, connected components, and strong connectivity. The shortest paths module covers Dijkstra's algorithm, Bellman-Ford algorithm, and their variants. The maximum flow module covers the Ford-Fulkerson algorithm, the Edmonds-Karp algorithm, and the Dinic's algorithm.
The string processing module introduces string searching, substring search, regular expressions, and data compression. The final module of the course covers advanced topics such as computational geometry, linear programming, and parallel algorithms.
By the end of the course, students will have gained a deeper understanding of advanced algorithms and data structures, as well as practical skills in algorithm design and implementation. They will be able to apply these skills to solve complex problems in a variety of domains such as computer science, engineering, and natural sciences.
Algorithms, Part II by Robert Sedgewick is a comprehensive online course available on Coursera. The course is designed for intermediate-level learners and covers advanced topics in algorithms and data structures. The course consists of the following 6 parts:
Undirected Graphs (4 lectures)
- Graph API
- Graph processing: DFS, BFS
- Connected components
- Symbol graphs
Directed Graphs (6 lectures)
- Digraph API
- Directed DFS, BFS
- Strong connectivity
- Topological sort
- Kosaraju's algorithm
- Dijkstra's algorithm
Minimum Spanning Trees (4 lectures)
- Cut property
- Greedy algorithm
- Kruskal's algorithm
- Prim's algorithm
Shortest Paths (6 lectures)
- Edge-weighted graphs
- Dijkstra's algorithm (again)
- Edge relaxation
- Bellman-Ford algorithm
- Acyclic graphs and DAG shortest paths
- Shortest paths in presence of negative cycles
Maximum Flow and Minimum Cut (4 lectures)
- Flow networks
- Ford-Fulkerson algorithm
- Maximum bipartite matching
- Edmonds-Karp algorithm
Radix Sorts, Tries, and Applications (5 lectures)
- LSD radix sort
- MSD radix sort
- Tries: R-way tries, TSTs
- Substring search
- Huffman compression
The course has a number of prerequisites, including basic programming skills (in Java or C++), familiarity with fundamental data structures (such as arrays, linked lists, and stacks), and an understanding of basic algorithm design principles (such as brute force and greedy algorithms). Additionally, it is recommended that students have completed the Algorithms, Part I course by Robert Sedgewick, or have equivalent knowledge.
As a former student of the Algorithms, Part II course by Robert Sedgewick on Coursera, I was impressed with the quality and depth of the course material. The course was the perfect continuation to the Algorithms, Part I course, and built upon the foundational concepts introduced in that course.
The lectures were well-organized and presented in a clear and concise manner, with plenty of examples and visuals to help reinforce the concepts. The course covered a wide range of topics, including graph algorithms, data compression, and string processing, among others. The lectures also included in-depth discussions on the theoretical underpinnings of the algorithms, which helped to provide a deeper understanding of the concepts.
The programming assignments were challenging, but also very rewarding. They provided hands-on experience with implementing the algorithms and data structures discussed in the lectures, and helped to reinforce the theoretical concepts. The assignments were also well-designed, with clear instructions and requirements, and were graded fairly by the instructors.
One of the strengths of the course was the community of learners and instructors. The discussion forums were an excellent resource for getting help with programming assignments, as well as for engaging in discussions on topics related to the course. The instructors were knowledgeable and responsive, and provided timely and helpful feedback on assignments.
Overall, I would highly recommend the Algorithms, Part II course to anyone who is interested in learning more about advanced algorithms and data structures. The course is challenging, but also very rewarding, and provides a solid foundation for further study in computer science. The knowledge and skills gained from the course are invaluable for anyone pursuing a career in the field.
At the time, the course has an average rating of 4.9 out of 5 stars based on over 1,802 ratings.
What you'll learn:
After completing the Algorithms, Part II course by Robert Sedgewick on Coursera, learners will have gained several skills, including:
Advanced knowledge of algorithms and data structures: The course covers advanced topics in algorithms and data structures, such as graph processing, shortest paths, maximum flow, string processing, data compression, and advanced topics.
Ability to implement complex algorithms: The course includes a substantial number of programming assignments that reinforce the concepts learned and help learners to implement complex algorithms.
Understanding of real-world applications: The course teaches learners how to apply algorithms and data structures to solve real-world problems, such as network analysis, data compression, and computational geometry.
Problem-solving skills: The course provides learners with the tools and techniques needed to approach and solve complex algorithmic problems.
Preparation for further study: The course provides a solid foundation for learners who wish to pursue further study in the field of computer science or related fields.
Overall, completing the Algorithms, Part II course by Robert Sedgewick on Coursera will equip learners with the advanced knowledge and skills needed to tackle complex algorithmic problems and apply them to real-world applications.
Robert Sedgewick is a computer science professor at Princeton University and a well-known author of several books on algorithms and data structures. He has been teaching computer science for over 40 years and is widely regarded as an authority in the field of algorithms and data structures.
Sedgewick has written numerous books, including "Algorithms in C++", "Algorithms in Java", and "Algorithms in Python", which are widely used as textbooks in universities and colleges around the world. His books are known for their clear explanations, comprehensive coverage, and practical approach to algorithms and data structures.
In addition to his work as an author, Sedgewick has also made significant contributions to the development of algorithms and data structures. He is the co-developer of the quicksort algorithm, which is one of the most widely used sorting algorithms in computer science.
Sedgewick is also a respected educator who has received numerous awards for his teaching and research, including the Karl V. Karlstom Outstanding Educator Award, the IEEE Computer Society's Taylor L. Booth Education Award, and the ACM SIGCSE Award for Outstanding Contributions to Computer Science Education.
Overall, Robert Sedgewick is a highly respected figure in the field of computer science and algorithms, with extensive experience and knowledge in teaching, research, and writing. His contributions to the development of algorithms and data structures have had a significant impact on the field, and his books and teaching have inspired countless students and researchers around the world.
The Algorithms, Part II course by Robert Sedgewick on Coursera has the following detailed requirements:
Programming experience: Students should have some experience with programming in a high-level language such as Java, C++, or Python. They should be familiar with basic programming concepts such as variables, loops, conditional statements, and functions.
Completion of Algorithms, Part I: Students should have completed the Algorithms, Part I course or have equivalent knowledge. This includes familiarity with basic data structures such as arrays, linked lists, stacks, and queues, as well as algorithms such as sorting and searching.
Computer and internet access: Students should have access to a computer with a reliable internet connection in order to watch lectures, complete assignments, and participate in discussion forums.
Knowledge of data structures: Students should have a solid understanding of data structures such as linked lists, stacks, queues, trees, and graphs. This includes knowledge of basic operations and implementation details for each data structure.
Knowledge of algorithms: Students should have a solid understanding of algorithms such as sorting, searching, and graph traversal. They should also be familiar with basic algorithm design techniques such as dynamic programming and greedy algorithms.
Mathematics background: While not strictly required, students with a basic background in mathematics, particularly probability and graph theory, will have an easier time understanding some of the more advanced topics covered in the course.
Time commitment: Students should be prepared to devote several hours per week to watching lectures, completing assignments, and participating in discussion forums. The course is designed to be challenging, and students will need to invest time and effort in order to fully understand the material.