1.6 - Course Schedule
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Practice Questions
Test your understanding with targeted questions
Define asymptotic complexity.
💡 Hint: Think about how algorithms perform on larger datasets.
What is Big O notation used for?
💡 Hint: Consider how we measure algorithm efficiency.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does Big O notation primarily measure?
💡 Hint: Think about how we gauge efficiency in algorithms.
True or False: Greedy algorithms always result in the optimal solution.
💡 Hint: Consider edge cases where greedy choices might lead to suboptimal results.
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Challenge Problems
Push your limits with advanced challenges
Propose a situation where a greedy algorithm might yield a suboptimal solution and discuss why that happens.
💡 Hint: Look into examples of change-making problems.
Design a small algorithm that uses dynamic programming to optimize a problem. Provide the steps involved.
💡 Hint: Think about how overlapping sub-problems can be stored.
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Reference links
Supplementary resources to enhance your learning experience.
- Understanding Algorithm Complexity
- What is Big O Notation?
- Graph Algorithms Overview
- Introduction to Greedy Algorithms
- Dynamic Programming Basics
- Course Syllabus on Algorithms
- C Programming Guidance
- Data Structure Fundamentals
- Understanding Sorting Algorithms
- Comprehensive Algorithms Textbook Overview