Data Structures detailed syllabus for Artificial Intelligence & Machine Learning (AM) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Artificial Intelligence & Machine Learning 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 Artificial Intelligence & Machine Learning (AM) 3rd Sem 2021 revision scheme. The detailed syllabus of data structures is as follows.
Course Objectives:
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Course Outcomes:
On completion of the course, the students will be able to:
- Illustrate the fundamental concepts of linear data structures stack and queue
- Illustrate searching and sorting algorithms and nonlinear data structure linked list
- Demonstrate non linear data structures – Trees
- Demonstrate non linear data structures – Graphs
Module 1:
Advanced C concepts:Concept of Pointers, structures Data Structures: Definition, Classification- Linear Data Structures, Non-Linear Data Structures, Abstract Data Type(ADT) Arrays: Array based sequences, operations-Traverse, insert, delete, search, update Stack: Introduction to Stack ADT – Array Representation of Stacks – Operations on a Stack – Applications of Stacks – Infix-to-Postfix Conversion. Queues: Introduction to Queue ADT – Array Representation of Queues – Operations on a Queue
Module 2:
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Module 3:
Trees: Definition – Basic Terminologies – Node, Parent, Child, Link, Root, Leaf, Level, Height of a tree and node, Depth of a tree and node, Degree of a tree and node, sibling, Ancestors, Path, Path Length. Binary Tree-Types of Binary Trees: Full, Complete Representations of a Binary Tree: using Linked List Traversal Algorithms- Inorder, Preorder, Postorder.
Binary Search Tree: Creation-Insertion -Traversal – Search – Deletion
Module 4:
Graphs: Terminologies – Vertex, Edge, Adjacent vertices, Self-loop, Parallel edges, Isolated vertex, Degree of vertex, Pendant vertex, Subgraph, Paths and Cycles. Types of Graphs – Directed, Undirected, Simple, Complete, Cyclic, Acyclic, Connected. Representation of Graphs – Set – Linked – Matrix Graph Traversals -BFS, DFS
Text Books:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Online Resources
- https://nptel.ac.in/courses/106103069
- http://www.tutorialspoint.com/cprogramming/
- https://www.programmingsimplified.com/c/data-structures
- https://www.programiz.com/dsa
For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning (AM), 2021 revision curriculum do visit Artificial Intelligence & Machine Learning 3rd Sem subject syllabuses for 2021 revision.
To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..
To see the results of Artificial Intelligence & Machine Learning (AM) of diploma 2021 revision curriculum do visit SITTTR diploma Artificial Intelligence & Machine Learning (AM) results..
For all Artificial Intelligence & Machine Learning academic calendars, visit Artificial Intelligence & Machine Learning all semesters academic calendar direct link.