This online course, "Algorithmic Toolbox," offered by University of California San Diego, is designed to equip learners with essential algorithmic techniques and efficient algorithm design skills necessary for solving computational problems. The comprehensive curriculum covers sorting, searching, divide and conquer, greedy algorithms, and dynamic programming, providing practical insights into their applications.
Throughout the course, learners will delve into a wide range of topics, including programming challenges, algorithmic warm-up, greedy algorithms, divide-and-conquer, and dynamic programming. You will practice solving computational problems, designing new algorithms, and implementing efficient solutions. The course provides a solid foundation for understanding and applying these fundamental algorithmic techniques in various practical scenarios.
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This course comprises modules covering programming challenges, algorithmic warm-up, greedy algorithms, divide-and-conquer, and dynamic programming. Each module provides detailed insights and practical skills essential for algorithmic problem-solving.
This module focuses on programming challenges, equipping learners with essential problem-solving skills and techniques. You will delve into topics such as solving the sum of two digits programming challenge, maximizing pairwise product programming challenge, and ace your next coding interview.
In this module, learners will explore the fundamental concepts of algorithmic warm-up, emphasizing the importance of studying algorithms. Topics covered include asymptotic notation, big-O notation, computing runtimes, and resources essential for understanding efficient algorithms.
This module delves into greedy algorithms, providing practical insights into solving problems such as the largest number, queue of patients, maximizing loot, and proving the correctness of greedy algorithms. Learners will also engage in programming assignments that enhance their understanding of greedy algorithms.
The divide-and-conquer module explores topics such as linear search, binary search, merge sort, non-comparison based sorting algorithms, and the master theorem. Learners will gain practical insights into solving problems using divide-and-conquer strategies and engage in programming assignments to reinforce their understanding.
This module delves into dynamic programming, covering topics such as the change problem, knapsack problems, edit distance, and reconstructing optimal alignments. Learners will develop practical skills in dynamic programming through engaging programming assignments and problem-solving scenarios.
Building on the concepts covered in the previous module, learners will explore advanced topics in dynamic programming, including knapsack with repetitions, knapsack without repetitions, and maximum value of an arithmetic expression. Practical application and problem-solving scenarios will reinforce their understanding of dynamic programming techniques.
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