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Sriram Sankaranarayanan
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
Read more
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
Read more
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Register for this course and see more details by visiting:
OpenCourser.com/course/likefg/dynamic
What's inside
Syllabus
Divide and Conquer Algorithms
We will formally cover divide and conquer algorithms as a design scheme and look at some divide and conquer algorithms we have encountered in the past. We will learn some divide and conquer algorithms for Integer Multiplication (Karatsuba’s Algorithm), Matrix Multiplication (Strassen’s Algorithm), Fast Fourier Transforms (FFTs), and Finding Closest Pair of Points.
Read more
Syllabus
Divide and Conquer Algorithms
We will formally cover divide and conquer algorithms as a design scheme and look at some divide and conquer algorithms we have encountered in the past. We will learn some divide and conquer algorithms for Integer Multiplication (Karatsuba’s Algorithm), Matrix Multiplication (Strassen’s Algorithm), Fast Fourier Transforms (FFTs), and Finding Closest Pair of Points.
Read more
Dynamic Programming Algorithms
In this module, you will learn about dynamic programming as a design principle for algorithms. We will provide a step-by-step approach to formulating a problem as a dynamic program and solving these problems using memoization. We will cover dynamic programming for finding longest common subsequences, Knapsack problem and some interesting dynamic programming applications.
Greedy Algorithms
In this module, we will learn about greedy algorithms. We will understand the basic design principles for greedy algorithms and learn about a few algorithms for greedy scheduling and Huffman codes. We will also learn some interesting cases when being greedy provides a guaranteed approximation to the actual solution.
Intractability and Supplement on Quantum Computing
P vs NP, Examples such as Travelling Salesperson Problem, Vertex Cover, 3-Coloring and others; Integer Linear Programming and Translating Problems into Integer Programming.
Good to know
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what to watch for , and
possible dealbreakers
Explores algorithm design techniques that are fundamental in software development and computer science
Teaches classic algorithms like divide and conquer, dynamic programming, and greedy algorithms
Introduces intractability and optimization problems, expanding learner's understanding of algorithm complexity
Offers advanced topics in data structures, deepening learner's understanding of data organization and manipulation
Can be taken for academic credit, providing learners the opportunity to enhance their credentials
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Reviews summary
Highly rated practical algorithms course
Learners say that Dynamic Programming, Greedy Algorithms is a well-received course that offers engaging assignments and challenging exams backed by a Stanford graduate instructor. According to students, the lectures and code assignments do a great job of explaining complex topics in dynamic programming and greedy algorithms, making them accessible to beginners.
Homework assignments are challenging but instructive.
"Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable."
"i went through this course just for a quick refresh on some basic algorithms and i ended completing all three courses from the specialization!"
"I eventually solved them, but would not reccommend the experience."
Stanford graduate instructor with clear explanations.
"Amazing opportunity to learn! A Stanford graduate as a professor, OMG! Super bright!"
"Dr. Sriram Sankaranarayanan did a good job introducing the basics of algorithms to us."
"the way he presents the material is super!"
Helpful lectures with well-organized content.
"A clear presentation of the topics. Well organized."
"The course gives vast content for the content."
"This course save me time on learning the dynamic programming."
Limited support on discussion boards and in the forum.
"What is the point of the discussion boards if no one responds?"
"Very good course. The only problem was the lack of support on the forum."
"I noticed that the was only one other post there from nine moths ago. That person never got a reply whereas I did."
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 Dynamic Programming, Greedy Algorithms with these
activities:
Create an Infographic on Divide-and-Conquer Algorithms
Show steps
Enhance your understanding and solidify your grasp of divide-and-conquer algorithms by creating a visual representation.
Show steps
-
Gather information and examples on various divide-and-conquer algorithms.
-
Design an infographic that visually explains the key concepts, steps, and applications of these algorithms.
-
Use clear and concise language, along with diagrams and illustrations, to convey the information effectively.
-
Share your infographic with your classmates or fellow learners for feedback and discussion.
Practice Sorting Algorithms
Show steps
Reinforce your understanding of sorting algorithms by practicing on various input datasets.
Browse courses on
Sorting
Show steps
-
Review the concept of sorting algorithms.
-
Implement different sorting algorithms (e.g., Merge Sort, Quick Sort, Insertion Sort) in your preferred programming language.
-
Write test cases with diverse input arrays to evaluate the correctness and efficiency of your implementations.
-
Analyze the time and space complexity of your implemented sorting algorithms.
Compile Resources on Divide-and-Conquer Algorithms
Show steps
Expand your understanding of divide-and-conquer algorithms by gathering and organizing relevant resources.
Show steps
-
Search for articles, tutorials, and online courses on divide-and-conquer algorithms.
-
Identify and select resources that provide valuable insights and explanations.
-
Organize the resources into a cohesive collection, categorized by topic or difficulty level.
-
Share your compilation with your classmates or fellow learners.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Walkthrough of Advanced Dynamic Programming Problems
Show steps
Enhance your problem-solving skills in dynamic programming by following guided tutorials that demonstrate advanced techniques.
Browse courses on
Dynamic programming
Show steps
-
Find resources or tutorials covering advanced dynamic programming techniques (e.g., memoized recursion, tabulation).
-
Select a problem that showcases the application of advanced dynamic programming techniques.
-
Follow the tutorial steps and understand the problem-solving approach.
-
Implement the solution in your preferred programming language.
Code Challenges on Dynamic Programming
Show steps
Sharpen your dynamic programming skills by solving coding challenges that require the application of advanced techniques.
Browse courses on
Dynamic programming
Show steps
-
Identify online platforms or resources that offer coding challenges on dynamic programming.
-
Select a problem that interests you and aligns with your learning goals.
-
Attempt to solve the problem using dynamic programming techniques.
-
Compare your solution with optimal solutions and analyze your approach.
Attend a Workshop on Data Structures and Algorithms
Show steps
Gain hands-on experience and insights by attending a workshop that focuses on advanced data structures and algorithms.
Browse courses on
Data Structures
Show steps
-
Research and identify workshops on data structures and algorithms.
-
Register for a workshop that aligns with your interests and learning goals.
-
Actively participate in the workshop sessions and engage in discussions.
-
Apply the knowledge and techniques learned in the workshop to your coursework.
Design and Implement a Pathfinding Algorithm
Show steps
Develop a deep understanding of pathfinding algorithms and their practical applications by designing and implementing one yourself.
Browse courses on
Algorithms
Show steps
-
Choose a specific pathfinding algorithm to focus on (e.g., Dijkstra's, A*).
-
Design and implement the algorithm in a programming language of your choice.
-
Craft various test cases to evaluate the performance and correctness of your implementation.
-
Analyze the results and identify areas for improvement or optimization.
Create an Infographic on Divide-and-Conquer Algorithms
Show steps
Enhance your understanding and solidify your grasp of divide-and-conquer algorithms by creating a visual representation.
Show steps
Show steps
Show steps
- Gather information and examples on various divide-and-conquer algorithms.
- Design an infographic that visually explains the key concepts, steps, and applications of these algorithms.
- Use clear and concise language, along with diagrams and illustrations, to convey the information effectively.
- Share your infographic with your classmates or fellow learners for feedback and discussion.
Practice Sorting Algorithms
Show steps
Reinforce your understanding of sorting algorithms by practicing on various input datasets.
Browse courses on
Sorting
Show steps
Show steps
Show steps
- Review the concept of sorting algorithms.
- Implement different sorting algorithms (e.g., Merge Sort, Quick Sort, Insertion Sort) in your preferred programming language.
- Write test cases with diverse input arrays to evaluate the correctness and efficiency of your implementations.
- Analyze the time and space complexity of your implemented sorting algorithms.
Compile Resources on Divide-and-Conquer Algorithms
Show steps
Expand your understanding of divide-and-conquer algorithms by gathering and organizing relevant resources.
Show steps
Show steps
Show steps
- Search for articles, tutorials, and online courses on divide-and-conquer algorithms.
- Identify and select resources that provide valuable insights and explanations.
- Organize the resources into a cohesive collection, categorized by topic or difficulty level.
- Share your compilation with your classmates or fellow learners.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Walkthrough of Advanced Dynamic Programming Problems
Show steps
Enhance your problem-solving skills in dynamic programming by following guided tutorials that demonstrate advanced techniques.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Find resources or tutorials covering advanced dynamic programming techniques (e.g., memoized recursion, tabulation).
- Select a problem that showcases the application of advanced dynamic programming techniques.
- Follow the tutorial steps and understand the problem-solving approach.
- Implement the solution in your preferred programming language.
Code Challenges on Dynamic Programming
Show steps
Sharpen your dynamic programming skills by solving coding challenges that require the application of advanced techniques.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify online platforms or resources that offer coding challenges on dynamic programming.
- Select a problem that interests you and aligns with your learning goals.
- Attempt to solve the problem using dynamic programming techniques.
- Compare your solution with optimal solutions and analyze your approach.
Attend a Workshop on Data Structures and Algorithms
Show steps
Gain hands-on experience and insights by attending a workshop that focuses on advanced data structures and algorithms.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Research and identify workshops on data structures and algorithms.
- Register for a workshop that aligns with your interests and learning goals.
- Actively participate in the workshop sessions and engage in discussions.
- Apply the knowledge and techniques learned in the workshop to your coursework.
Design and Implement a Pathfinding Algorithm
Show steps
Develop a deep understanding of pathfinding algorithms and their practical applications by designing and implementing one yourself.
Browse courses on
Algorithms
Show steps
Show steps
Show steps
- Choose a specific pathfinding algorithm to focus on (e.g., Dijkstra's, A*).
- Design and implement the algorithm in a programming language of your choice.
- Craft various test cases to evaluate the performance and correctness of your implementation.
- Analyze the results and identify areas for improvement or optimization.
Career center
Learners who complete Dynamic Programming, Greedy Algorithms will develop knowledge and skills
that may be useful to these careers:
Algorithm Engineer
Algorithm Engineer
Algorithm Engineers research and develop new algorithms to solve complex problems. The algorithms and techniques covered in this course provide a strong foundation for Algorithm Engineers seeking to advance their knowledge of algorithm design.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of scientific fields, including computer science. This course provides a strong foundation in the algorithms and techniques used in computer science research, making it a valuable asset for Research Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning systems. This course provides a strong foundation in the algorithms and techniques used in machine learning, making it a valuable asset for Machine Learning Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in the algorithms and techniques used in quantitative finance.
Software Architect
Software Architect
Software Architects design and develop the overall architecture of software systems. This course provides a strong foundation in the algorithms and data structures used in software architecture.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. The algorithms and techniques covered in this course provide a strong foundation for Operations Research Analysts seeking to apply these techniques to real-world problems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and data infrastructure. This course provides a strong foundation in the algorithms and data structures used in data engineering.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies and applications. This course may be useful for Computer Scientists seeking to advance their knowledge of algorithm design techniques.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers seeking to build a foundation in algorithms and data structures, which are fundamental building blocks of software systems.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. This course may be useful for Business Analysts seeking to build a stronger foundation in data analysis techniques.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to provide insights to businesses and organizations. This course may be useful for Data Analysts seeking to build a stronger foundation in algorithms and data structures.
Data Scientist
Data Scientist
Data Scientists use algorithms and other techniques to extract insights from data to aid decision-making. As algorithms are an important tool in the Data Scientist's toolbelt, this course may be useful for cultivating a deeper understanding of the algorithms used in data science.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. This course may provide a foundation in the algorithms and techniques used in product development.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may provide a foundation in the algorithms and techniques used in project management.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. This course may provide a foundation in the algorithms and techniques used in consulting.
For more career information including salaries, visit:
OpenCourser.com/course/likefg/dynamic
Reading list
We've selected 13 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
Dynamic Programming, Greedy Algorithms.
Comprehensive introduction to algorithms, data structures, and their applications. It great resource for students who want to learn more about the theoretical foundations of computer science.
Provides a comprehensive overview of dynamic programming and combinatorial optimization. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of greedy algorithms. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of data structures and algorithms in Python. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of algorithm design. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of algorithmics. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of quantum computing. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of quantum computation and quantum information. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of linear programming and network flows. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of integer programming. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of combinatorial optimization. It great resource for students who want to learn more about these topics in depth.
Provides a comprehensive overview of approximation algorithms. It great resource for students who want to learn more about these topics in depth.
Save
Provides a comprehensive overview of network flows. It great resource for students who want to learn more about these topics in depth.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/likefg/dynamic
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Effort
eight to fifteen hours a week for four weeks
Level
Advanced
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Sriram Sankaranarayanan
Language
English
Transcripts
Español
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Good to know
Explores algorithm design techniques that are fundamental in software development and computer science
Teaches classic algorithms like divide and conquer, dynamic programming, and greedy algorithms
Introduces intractability and optimization problems, expanding learner's understanding of algorithm complexity
Offers advanced topics in data structures, deepening learner's understanding of data organization and manipulation
Can be taken for academic credit, providing learners the opportunity to enhance their credentials
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