Lesson plan according to AKTU Lucknow(lecture-1) DAA

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Lesson Plan- 5th Semester(2024-25)

Design and analysis of Algorithms

Here’s a comprehensive 40-lecture lesson plan for the “Design and Analysis of Algorithms” course based on typical Computer Science Engineering syllabi, tailored to match the AKTU guidelines. Each lecture can be 1 hour long, and the course is structured into units with topics divided across them.

Unit 1: Introduction to Algorithms (6 Lectures)

  1. Lecture 1: Introduction to Algorithm – Definition, Characteristics, and Properties
  2. Lecture 2: Fundamentals of Algorithm Design: Problem-Solving Methods
  3. Lecture 3: Time and Space Complexity: Asymptotic Notations (Big O, Big Omega, Theta)
  4. Lecture 4: Analyzing Recursive Algorithms: Recurrence Relations and Solutions
  5. Lecture 5: Growth of Functions – Best, Average, and Worst-Case Analysis
  6. Lecture 6: Amortized Analysis

Unit 2: Divide and Conquer (6 Lectures)

  1. Lecture 7: Introduction to Divide and Conquer – General Method
  2. Lecture 8: Binary Search – Design and Analysis
  3. Lecture 9: Merge Sort – Design and Complexity Analysis
  4. Lecture 10: Quick Sort – Design and Complexity Analysis
  5. Lecture 11: Matrix Multiplication – Strassen’s Algorithm
  6. Lecture 12: Applications of Divide and Conquer

Unit 3: Greedy Algorithms (7 Lectures)

  1. Lecture 13: Introduction to Greedy Algorithms – General Method
  2. Lecture 14: Activity Selection Problem
  3. Lecture 15: Huffman Coding
  4. Lecture 16: Kruskal’s Algorithm for Minimum Spanning Tree
  5. Lecture 17: Prim’s Algorithm for Minimum Spanning Tree
  6. Lecture 18: Dijkstra’s Algorithm for Shortest Path
  7. Lecture 19: Limitations of Greedy Algorithms

Unit 4: Dynamic Programming (8 Lectures)

  1. Lecture 20: Introduction to Dynamic Programming – General Method
  2. Lecture 21: Comparison with Divide and Conquer Approach
  3. Lecture 22: Longest Common Subsequence (LCS)
  4. Lecture 23: 0/1 Knapsack Problem
  5. Lecture 24: Matrix Chain Multiplication
  6. Lecture 25: Floyd-Warshall Algorithm for All-Pairs Shortest Paths
  7. Lecture 26: Bellman-Ford Algorithm for Shortest Path
  8. Lecture 27: Applications of Dynamic Programming

Unit 5: Backtracking and Branch and Bound (7 Lectures)

  1. Lecture 28: Introduction to Backtracking – General Method
  2. Lecture 29: N-Queens Problem
  3. Lecture 30: Graph Coloring Problem
  4. Lecture 31: Hamiltonian Cycle Problem
  5. Lecture 32: Introduction to Branch and Bound – General Method
  6. Lecture 33: 0/1 Knapsack Problem using Branch and Bound
  7. Lecture 34: Traveling Salesman Problem (TSP) using Branch and Bound

Unit 6: Advanced Topics in Algorithms (6 Lectures)

  1. Lecture 35: Introduction to NP-Completeness: P, NP, NP-Hard, NP-Complete Classes
  2. Lecture 36: Cook’s Theorem and Introduction to Polynomial Reductions
  3. Lecture 37: Approximation Algorithms – Overview and Techniques
  4. Lecture 38: Randomized Algorithms – Introduction and Techniques
  5. Lecture 39: Introduction to Parallel Algorithms
  6. Lecture 40: Review and Open Problems in Algorithm Design

Key Points:

  • Assessments: Regular quizzes, mid-term exams, and assignments should be incorporated.
  • Practical Sessions: Accompany lectures with lab sessions where students implement algorithms in languages like C++, Python, or Java.
  • Textbooks: Recommended references include “Introduction to Algorithms” by Cormen, Leiserson, Rivest, and Stein (CLRS) and “Algorithm Design” by Kleinberg and Tardos”

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