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Algorithms
• Introduction to Algorithms
• What is an Algorithm?
• Importance of Algorithms in Computer
Science
Characteristics of Algorithms
• 1. Unambiguous
• 2. Well-defined inputs and outputs
• 3. Finite steps
• 4. Effectiveness
• 5. Language-independent
Types of Algorithms
• 1. Searching Algorithms (Linear Search, Binary
Search)
• 2. Sorting Algorithms (Bubble Sort, Insertion
Sort, Merge Sort)
• 3. Recursive Algorithms
• 4. Greedy Algorithms
• 5. Dynamic Programming
Flowcharts and Pseudocode
• Understanding flowcharts
• Pseudocode basics
• Example: Algorithm for adding two numbers
Example: Linear Search Algorithm
• 1. Start
• 2. Input array and target element
• 3. Compare each element
• 4. If found, return index
• 5. If not found, return -1
• 6. End
Example: Bubble Sort Algorithm
• 1. Start
• 2. Repeat until the array is sorted
• 3. Compare adjacent elements
• 4. Swap if out of order
• 5. Repeat for all elements
• 6. End
Algorithm Efficiency
• Time Complexity (Big O Notation)
• Space Complexity
• Best, Worst, and Average Cases
Applications of Algorithms
• 1. AI and Machine Learning
• 2. Data Compression
• 3. Network Routing
• 4. Cybersecurity
• 5. Robotics
Conclusion
• Recap of algorithms and their importance
• Practice writing and optimizing algorithms
• Use real-world problems to improve
algorithmic thinking

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Algorithm_Presentation_Ixjkmcfsdnm,vznkslds,nf

  • 1. Algorithms • Introduction to Algorithms • What is an Algorithm? • Importance of Algorithms in Computer Science
  • 2. Characteristics of Algorithms • 1. Unambiguous • 2. Well-defined inputs and outputs • 3. Finite steps • 4. Effectiveness • 5. Language-independent
  • 3. Types of Algorithms • 1. Searching Algorithms (Linear Search, Binary Search) • 2. Sorting Algorithms (Bubble Sort, Insertion Sort, Merge Sort) • 3. Recursive Algorithms • 4. Greedy Algorithms • 5. Dynamic Programming
  • 4. Flowcharts and Pseudocode • Understanding flowcharts • Pseudocode basics • Example: Algorithm for adding two numbers
  • 5. Example: Linear Search Algorithm • 1. Start • 2. Input array and target element • 3. Compare each element • 4. If found, return index • 5. If not found, return -1 • 6. End
  • 6. Example: Bubble Sort Algorithm • 1. Start • 2. Repeat until the array is sorted • 3. Compare adjacent elements • 4. Swap if out of order • 5. Repeat for all elements • 6. End
  • 7. Algorithm Efficiency • Time Complexity (Big O Notation) • Space Complexity • Best, Worst, and Average Cases
  • 8. Applications of Algorithms • 1. AI and Machine Learning • 2. Data Compression • 3. Network Routing • 4. Cybersecurity • 5. Robotics
  • 9. Conclusion • Recap of algorithms and their importance • Practice writing and optimizing algorithms • Use real-world problems to improve algorithmic thinking