Algorithmic Design and Techniques
Master essential algorithmic techniques and solve computational problems efficiently through hands-on programming challenges from UC San Diego.
Master essential algorithmic techniques and solve computational problems efficiently through hands-on programming challenges from UC San Diego.
This comprehensive course teaches fundamental algorithmic techniques for solving computational problems efficiently. Students learn key concepts including sorting, searching, divide and conquer, greedy algorithms, and dynamic programming. The course emphasizes practical implementation through programming challenges, teaching students to design and implement efficient solutions that execute in under a second. Real-world applications are explored, from genomic studies to automated spelling corrections. The curriculum combines theoretical understanding with hands-on coding practice, ensuring students can both design algorithms and implement them effectively.
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What you'll learn
Master essential algorithmic techniques for efficient problem solving
Implement sorting and searching algorithms with optimal performance
Apply divide and conquer strategies to break down complex problems
Develop efficient solutions using greedy algorithms
Understand and implement dynamic programming solutions
Optimize code for faster execution
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course provides a comprehensive introduction to algorithmic techniques essential for solving computational problems efficiently. Students learn fundamental approaches including greedy algorithms, divide and conquer strategies, and dynamic programming. The curriculum covers practical applications from database searching to genomic studies, with emphasis on implementing solutions that execute efficiently. Through hands-on programming challenges, students learn to design, test, and debug their implementations. The course is structured to build both theoretical understanding and practical coding skills, preparing students for real-world algorithm design and implementation challenges.
Welcome
Module 1
Introduction
Module 2
Greedy Algorithms
Module 3
Divide-and-Conquer
Module 4
Dynamic Programming
Module 5
Fee Structure
Payment options
Financial Aid
Instructors
8 Courses
Mathematics and Theoretical Computer Science Prodigy
Daniel Mertz Kane serves as a full professor with a joint appointment in Mathematics and Computer Science at the University of California, San Diego, where he has established himself as a leading researcher in theoretical computer science, combinatorics, and number theory. Born in 1986 to academic parents in Madison, Wisconsin, his extraordinary mathematical talent emerged early, mastering K-9 mathematics by third grade and conducting university-level research under Ken Ono while still in high school. His academic achievements include two gold medals in the International Mathematical Olympiad (2002, 2003), being one of only eight people in history to become a four-time Putnam Fellow, and winning the 2007 Morgan Prize. After earning dual bachelor's degrees from MIT in mathematics with computer science and physics (2007), he completed his Ph.D. at Harvard under Barry Mazur in 2011. His research contributions span multiple areas, including groundbreaking work in computational statistics, Boolean functions, and machine learning, earning him numerous awards including the IBM Pat Goldberg Memorial and PODS best paper awards. He recently co-authored a book on robust statistics with Ilias Diakonikolas, to be published by Cambridge University Press
13 Courses
Distinguished Computer Scientist and Algorithms Expert
Alexander S. Kulikov serves as a visiting professor at the University of California, San Diego, and a leading research fellow at the Steklov Institute of Mathematics in St. Petersburg. His academic journey includes earning his Ph.D. in 2009 and Dr.Sci. in 2017 from the St. Petersburg Department of Steklov Institute of Mathematics. His research focuses on algorithms for NP-hard problems and circuit complexity, with significant contributions to computational complexity theory and algorithm design. He has authored several influential educational resources, including "Learning Algorithms Through Programming and Puzzle Solving" and co-created major online courses on platforms like Coursera and edX. His teaching experience spans more than eight years, during which he has developed innovative approaches to algorithms education. Currently at JetBrains as a researcher, he continues to advance the field through his work on algorithmic problem-solving and computational complexity, while maintaining his academic connections through his visiting professorship at UCSD and research position at Steklov Institute.
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