Design and Analysis of Algorithms detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Data Science 3rd Sem scheme and its subjects, do visit AI&DS 3rd Sem 2021 regulation scheme. The detailed syllabus of design and analysis of algorithms is as follows.
Course Objectives:
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Unit I
INTRODUCTION 8 Notion of an Algorithm – Fundamentals of Algorithmic Problem Solving – Important Problem Types -Fundamentals of the Analysis of Algorithm Efficiency – Analysis Framework – Asymptotic Notations and their properties – Empirical analysis – Mathematical analysis of Recursive and Non-recursive algorithms – Visualization.
Unit II
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Unit III
DYNAMIC PROGRAMMING AND GREEDY TECHNIQUE 10 Dynamic programming – Principle of optimality – Coin changing problem – Warshalls and Floyds algorithms – Optimal Binary Search Trees – Multi stage graph – Knapsack Problem and Memory functions. Greedy Technique – Dijkstras algorithm – Huffman Trees and codes – 0/1 Knapsack problem.
Unit IV
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Unit V
LIMITATIONS OF ALGORITHM POWER 9 Lower – Bound Arguments – P, NP, NP- Complete and NP Hard Problems. Backtracking – N-Queen problem – Hamiltonian Circuit Problem – Subset Sum Problem. Branch and Bound – LIFO Search and FIFO search – Assignment problem – Knapsack Problem – Traveling Salesman Problem -Approximation Algorithms for NP-Hard Problems – Traveling Salesman problem – Knapsack problem.
Practical Exercises:
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Course Outcomes:
At the end of this course, the students will be able to:
- Analyze the efficiency of recursive and non-recursive algorithms mathematically
- Analyze the efficiency of brute force, divide and conquer, decrease and conquer, Transform and conquer algorithmic techniques
- Implement and analyze the problems using dynamic programming and greedy algorithmic techniques.
- Solve the problems using iterative improvement techniques for optimization.
- Compute the limitations of algorithmic power and solve the problems using backtracking and branch and bound techniques.
Text Books:
- Anany Levitin, Introduction to the Design and Analysis of Algorithms, Third Edition, Pearson Education, 2012.
Reference Books:
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For detailed syllabus of all other subjects of Artificial Intelligence & Data Science, 2021 regulation curriculum do visit AI&DS 3rd Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.