CD3291: Data Structures and Algorithms syllabus for AI&ML 2021 regulation

Data Structures and Algorithms detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&ML 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 & Machine Learning 3rd Sem scheme and its subjects, do visit AI&ML 3rd Sem 2021 regulation scheme. The detailed syllabus of data structures and algorithms is as follows.

Course Objectives:

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Unit I

ABSTRACT DATA TYPES 9 Abstract Data Types (ADTs) – ADTs and classes – introduction to OOP – classes in Python -inheritance – namespaces – shallow and deep copying Introduction to analysis of algorithms – asymptotic notations – divide & conquer – recursion -analyzing recursive algorithms

Unit II

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Unit III

SORTING AND SEARCHING 9 Bubble sort – selection sort – insertion sort – merge sort – quick sort – analysis of sorting algorithms – linear search – binary search – hashing – hash functions – collision handling – load factors, rehashing, and efficiency

Unit IV

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Unit V

GRAPH STRUCTURES 9 Graph ADT – representations of graph – graph traversals – DAG – topological ordering – greedy algorithms – dynamic programming – shortest paths – minimum spanning trees – introduction to complexity classes and intractability At the end of the course, the student should be able to:

1. Explain abstract data types
2. Design, implement, and analyze linear data structures, such as lists, queues, and stacks, according to the needs of different applications
3. Design, implement, and analyze efficient tree structures to meet requirements such as searching, indexing, and sorting
4. Model problems as graph problems and implement efficient graph algorithms to solve them

Text Books:

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Reference Books:

1. Lee, Kent D., Hubbard, Steve, Data Structures and Algorithms with Python Springer Edition
2. Rance D. Necaise, Data Structures and Algorithms Using Python, John Wiley & Sons, 2011
3. Aho, Hopcroft, and Ullman, Data Structures and Algorithms, Pearson Education, 1983.
4. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, Introduction to Algorithms”, Second Edition, McGraw Hill, 2002.
5. Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, Fourth Edition, Pearson Education, 2014

For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning, 2021 regulation curriculum do visit AI&ML 3rd Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.