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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