Vision

To bring forth technically versatile, Research oriented, Industry ready engineers in the field of Artificial Intelligence and Machine Learning.

Mission

• Facilitate modern infrastructure and versatile learning resources to produce self-sustainable professionals

•  Facilitate project based learning and skill upgradation through industry  collaborations

•  Inculcate professional ethics, leadership qualities and practice lifelong learning

• PSO1: Graduates will have the ability to adapt, contribute and innovate ideas in the field of Artificial Intelligence and Machine Learning.

• PSO2: To provide a concrete foundation and enrich their abilities to qualify for Employment, Higher studies and Research in various domains of Artificial Intelligence and Machine Learning such as Data Science, Computer Vision, Natural Language Processing with Ethical Values.

• PSO3: Graduates will acquire the practical proficiency with niche technologies and open-source platforms and to become Entrepreneur in the domain Artificial Intelligence and Machine Learning.

• PEO1: Attain proficiency in professional practice

• PEO2: Practice technical skills to identify, analyze and solve complex problems related to Artificial Intelligence and Machine Learning.

• PEO3: Emerge as an Individual or a team member with societal concerns, ethics and motivated for holistic learning.

Module 1

INTRODUCTION: What is an Algorithm?, Fundamentals of Algorithmic Problem Solving.

FUNDAMENTALS OF THE ANALYSIS OF ALGORITHM EFFICIENCY: Analysis Framework, Asymptotic Notations and Basic Efficiency Classes, Mathematical Analysis of Non recursive Algorithms, Mathematical Analysis of Recursive Algorithms.

BRUTE FORCE APPROACHES: Selection Sort and Bubble Sort, Sequential Search and Brute Force String Matching.

Module 2

BRUTE FORCE APPROACHES (contd..): Exhaustive Search (Travelling Salesman probem and Knapsack Problem).

DECREASE-AND-CONQUER: Insertion Sort, Topological Sorting.

DIVIDE AND CONQUER: Merge Sort, Quick Sort, Binary Tree Traversals, Multiplication of Large Integers and Strassen’s Matrix Multiplication.

Module 3

TRANSFORM-AND-CONQUER: Balanced Search Trees, Heaps and Heapsort.

SPACE-TIME TRADEOFFS: Sorting by Counting: Comparison counting sort, Input Enhancement in String Matching: Horspool’s Algorithm.

Module 4

DYNAMIC PROGRAMMING: Three basic examples, The Knapsack Problem and Memory Functions, Warshall’s and Floyd’s Algorithms.

THE GREEDY METHOD: Prim’s Algorithm, Kruskal’s Algorithm, Dijkstra’s Algorithm, Huffman Trees and Codes.

Module 5

LIMITATIONS OF ALGORITHMIC POWER: Decision Trees, P, NP, and NP-Complete Problems.

COPING WITH LIMITATIONS OF ALGORITHMIC POWER: Backtracking (n-Queens problem, Subset-sum problem), Branch-and-Bound (Knapsack problem), Approximation algorithms for NP-Hard problems (Knapsack problem).