We may earn an affiliate commission when you visit our partners.
Tim Roughgarden
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).
Register for this course and see more details by visiting:
OpenCourser.com/course/nvyyuf/greedy
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
Read more
Syllabus
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
Read more
Week 3
Huffman codes; introduction to dynamic programming.
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid foundation in greedy algorithms and dynamic programming
Taught by Tim Roughgarden, a recognized expert in algorithm design and analysis
Covers fundamental concepts used in computer science and software engineering
Requires a strong understanding of mathematics and programming
May require additional background reading or external resources for a deeper understanding of advanced topics
Students with no prior experience in algorithm design may find the pace challenging
Save this course
Save Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming to your list so you can find it easily later:
Save
Save
Reviews summary
Advanced algorithms course
Learners say that this course is a challenging but rewarding experience perfect for improving their intuition about algorithms. They particularly enjoy the smaller videos and programming assignments. Students are appreciative of the pacing and Professor Roughgarden's teaching style. Overall, those that reviewed this course found it to be positive and helpful for deepening their understanding of algorithms.
The course pace is good.
"The pace is not fast enough to get lost and not so slow to insult your intelligence."
"Great Pace - good examples - always going to the point - clear and providing good information."
"I liked the idea of splitting those 20-30 minutes videos from parts 1 and 2 into smaller ones."
The video lectures are short and helpful.
"I liked the idea of splitting those 20-30 minutes videos from parts 1 and 2 into smaller ones."
"Professor Tim Roughgarden provides an intuitive perspective to Dynamic Programming."
"The instructor makes you understand is really great."
"He's also really motivating."
The programming assignments are difficult but fun.
"Programming assignments were the most challenging but fun part!"
"This course has the most intuitive explanation of dynamic programming that I've ever seen."
"The end of the course (dynamic programming) was harder and kicked my butt a little, but I made it out with a much better grasp of the topics."
"I'm finally able to come up with my own dynamic programming solutions after taking this course, something I've never been able to do before, and it's all thanks to professor TIm Roughgarden's amazing and unique approach!"
Course content is challenging but very rewarding.
"The hurdles were high, but the content was very rewarding."
"This course is very challenging but rewarding."
"One of the most challenging course I've ever taken."
Professor Roughgarden is an amazing instructor.
"Professor Roughgarden provides an intuitive perspective to Dynamic Programming."
"The instructor makes you understand is really great."
"Great course and best computer science instructor I've ever had."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming with these
activities:
Organize course materials
Show steps
Improve understanding and retention by organizing course materials systematically.
Browse courses on
Greedy Algorithms
Show steps
-
Gather lecture notes, slides, and assignments.
-
Create a structured system for organizing the materials.
Review 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein
Show steps
Strengthen the theoretical understanding of greedy algorithms and dynamic programming by reviewing a foundational textbook.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
-
Read the relevant chapters on greedy algorithms and dynamic programming.
-
Solve practice problems from the book.
Participate in peer study sessions
Show steps
Enhance understanding and problem-solving abilities by collaborating with peers.
Browse courses on
Greedy Algorithms
Show steps
-
Find a group of classmates with similar interests.
-
Set regular meeting times and discuss course material.
-
Work together on practice problems or projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on algorithmic thinking
Show steps
Improve algorithmic thinking and problem-solving skills by solving practice problems related to greedy algorithms and dynamic programming.
Browse courses on
Greedy Algorithms
Show steps
-
Identify the main concepts of greedy algorithms and dynamic programming.
-
Solve problems from online resources or textbooks.
-
Analyze the solutions and identify patterns.
Explain a greedy algorithm or dynamic programming technique
Show steps
Deepen understanding of a greedy algorithm or dynamic programming technique by explaining it to others.
Show steps
-
Choose a specific greedy algorithm or dynamic programming technique.
-
Research and understand the algorithm thoroughly.
-
Create a presentation, video, or written explanation.
-
Share your explanation with others and seek feedback.
Develop a mobile app to solve scheduling problems
Show steps
Apply greedy algorithms to a practical problem by developing a mobile app for solving scheduling problems.
Browse courses on
Greedy Algorithms
Show steps
-
Identify specific scheduling problems to target.
-
Design and implement a greedy algorithm for solving the problems.
-
Develop a mobile app that incorporates the greedy algorithm.
-
Test and refine the app based on user feedback.
Organize course materials
Show steps
Improve understanding and retention by organizing course materials systematically.
Browse courses on
Greedy Algorithms
Show steps
Show steps
Show steps
- Gather lecture notes, slides, and assignments.
- Create a structured system for organizing the materials.
Review 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein
Show steps
Strengthen the theoretical understanding of greedy algorithms and dynamic programming by reviewing a foundational textbook.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters on greedy algorithms and dynamic programming.
- Solve practice problems from the book.
Participate in peer study sessions
Show steps
Enhance understanding and problem-solving abilities by collaborating with peers.
Browse courses on
Greedy Algorithms
Show steps
Show steps
Show steps
- Find a group of classmates with similar interests.
- Set regular meeting times and discuss course material.
- Work together on practice problems or projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on algorithmic thinking
Show steps
Improve algorithmic thinking and problem-solving skills by solving practice problems related to greedy algorithms and dynamic programming.
Browse courses on
Greedy Algorithms
Show steps
Show steps
Show steps
- Identify the main concepts of greedy algorithms and dynamic programming.
- Solve problems from online resources or textbooks.
- Analyze the solutions and identify patterns.
Explain a greedy algorithm or dynamic programming technique
Show steps
Deepen understanding of a greedy algorithm or dynamic programming technique by explaining it to others.
Show steps
Show steps
Show steps
- Choose a specific greedy algorithm or dynamic programming technique.
- Research and understand the algorithm thoroughly.
- Create a presentation, video, or written explanation.
- Share your explanation with others and seek feedback.
Develop a mobile app to solve scheduling problems
Show steps
Apply greedy algorithms to a practical problem by developing a mobile app for solving scheduling problems.
Browse courses on
Greedy Algorithms
Show steps
Show steps
Show steps
- Identify specific scheduling problems to target.
- Design and implement a greedy algorithm for solving the problems.
- Develop a mobile app that incorporates the greedy algorithm.
- Test and refine the app based on user feedback.
Career center
Learners who complete Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming will develop knowledge and skills
that may be useful to these careers:
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical models and algorithms to improve the efficiency of business processes. The course's focus on greedy algorithms and dynamic programming can help an Operations Research Analyst develop the skills needed to solve complex optimization problems.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. The course's focus on greedy algorithms and dynamic programming can help a Data Scientist develop the skills needed to analyze data and develop predictive models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. The course's focus on greedy algorithms and dynamic programming can help a Software Engineer develop the skills needed to design and implement efficient software.
Computer Scientist
Computer Scientist
A Computer Scientist designs, develops, and implements computer software and systems. The course's focus on greedy algorithms and dynamic programming can help a Computer Scientist develop the skills needed to design and implement efficient algorithms.
Actuary
Actuary
An Actuary uses mathematics to assess risk and uncertainty. The course's focus on greedy algorithms and dynamic programming can help an Actuary develop the skills needed to develop and price insurance products.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. The course's focus on greedy algorithms and dynamic programming can help a Statistician develop the skills needed to design and conduct statistical studies.
Financial Analyst
Financial Analyst
A Financial Analyst provides financial advice to individuals and organizations. The course's focus on greedy algorithms and dynamic programming can help a Financial Analyst develop the skills needed to make sound investment decisions.
Management Consultant
Management Consultant
A Management Consultant helps organizations improve their performance. The course's focus on greedy algorithms and dynamic programming can help a Management Consultant develop the skills needed to analyze business problems and develop solutions.
Systems Analyst
Systems Analyst
A Systems Analyst designs and implements computer systems. The course's focus on greedy algorithms and dynamic programming can help a Systems Analyst develop the skills needed to design and implement efficient systems.
Project Manager
Project Manager
A Project Manager plans and executes projects. The course's focus on greedy algorithms and dynamic programming can help a Project Manager develop the skills needed to manage projects efficiently.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and develops solutions to improve efficiency. The course's focus on greedy algorithms and dynamic programming can help a Business Analyst develop the skills needed to identify and solve business problems.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. The course's focus on greedy algorithms and dynamic programming can help a Database Administrator develop the skills needed to optimize database performance.
Network Administrator
Network Administrator
A Network Administrator manages and maintains computer networks. The course's focus on greedy algorithms and dynamic programming can help a Network Administrator develop the skills needed to optimize network performance.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects computer systems and networks from unauthorized access. The course's focus on greedy algorithms and dynamic programming can help an Information Security Analyst develop the skills needed to design and implement security measures.
Operations Manager
Operations Manager
An Operations Manager plans and directs the operations of an organization. The course's focus on greedy algorithms and dynamic programming can help an Operations Manager develop the skills needed to optimize business processes.
For more career information including salaries, visit:
OpenCourser.com/course/nvyyuf/greedy
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming.
This classic textbook covers greedy algorithms, minimum spanning trees, and dynamic programming in great detail. It valuable reference for anyone interested in learning more about these topics.
This textbook provides a comprehensive overview of algorithms, including greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to learn more about these topics in a clear and concise way.
Classic work on minimum spanning trees. It provides a comprehensive overview of the theory and practice of MSTs, including algorithms for finding MSTs and applications to clustering.
Classic work on dynamic programming. It provides a comprehensive overview of the theory and practice of dynamic programming, including applications to knapsack problems, sequence alignment, and optimal binary search trees.
Provides a practical guide to algorithm design. It includes chapters on greedy algorithms, minimum spanning trees, and dynamic programming, as well as other topics.
This textbook provides a comprehensive overview of data structures and algorithms, including greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to learn more about these topics in a Java-centric way.
Provides a concise overview of algorithms and data structures, including greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to learn more about these topics in a clear and concise way.
Save
Provides a collection of algorithmic puzzles, including puzzles related to greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to learn more about these topics in a fun and challenging way.
Provides a collection of coding interview questions, including questions related to greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to prepare for job interviews in the tech industry.
Provides a comprehensive guide to cracking the coding interview, including tips on how to answer questions related to greedy algorithms, minimum spanning trees, and dynamic programming. It good choice for students who want to prepare for job interviews in the tech industry.
Classic work on software engineering. It provides insights into the challenges of software development, including the challenges of designing and implementing algorithms. It good choice for students who want to learn more about the software development process.
Save
Classic work on peopleware, the human side of software development. It provides insights into the challenges of working in teams, including the challenges of communication and collaboration. It good choice for students who want to learn more about the people side of software development.
Practical guide to becoming a better programmer. It provides insights into the challenges of software development, including the challenges of designing and implementing algorithms. It good choice for students who want to learn more about the software development process.
Save
Guide to the soft skills that software developers need to succeed. It provides insights into the challenges of working in teams, including the challenges of communication and collaboration. It good choice for students who want to learn more about the people side of software development.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nvyyuf/greedy
Share
Similar courses
Here are nine courses similar to
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming.
Algorithms Data Structures in Java #1 (+INTERVIEW QUESTIONS)
OpenCourser.com/course/alg19j/algorithms
Algorithms Data Structures in Java #1 (+INTERVIEW...
Expression Trees in C#
OpenCourser.com/course/c9wk3g/expression
Expression Trees in C#
Ordered Data Structures
OpenCourser.com/course/2i4xtc/ordered
Ordered Data Structures
Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms
OpenCourser.com/course/0l7zmq/data
Data Structures & Algorithms III: AVL and 2-4 Trees,...
Trees and Graphs: Basics
OpenCourser.com/course/i8v24i/trees
Trees and Graphs: Basics
Algorithms and Data Structures in Python (INTERVIEW Q&A)
OpenCourser.com/course/ejkoch/algorithms
Algorithms and Data Structures in Python (INTERVIEW Q&A)
Classification Using Tree Based Models
OpenCourser.com/course/s8b5og/classification
Classification Using Tree Based Models
Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps
OpenCourser.com/course/2rkzd8/data
Data Structures & Algorithms II: Binary Trees, Heaps,...
Predict Employee Turnover with scikit-learn
OpenCourser.com/course/mxfxxs/predict
Predict Employee Turnover with scikit-learn
Effort
4 weeks of study, 4-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Stanford University
Instructor
Tim Roughgarden
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Algorithms. It's comprised of four courses:
- Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
- Graph Search, Shortest Paths, and Data Structures
- Divide and Conquer, Sorting and Searching, and Randomized Algorithms
- Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (viewing)
Good to know
Provides a solid foundation in greedy algorithms and dynamic programming
Taught by Tim Roughgarden, a recognized expert in algorithm design and analysis
Covers fundamental concepts used in computer science and software engineering
Requires a strong understanding of mathematics and programming
May require additional background reading or external resources for a deeper understanding of advanced topics
Students with no prior experience in algorithm design may find the pace challenging
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser