Vision and Mission of the Department

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

Program Specific Outcomes (PSOs):

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.

Program Educational Objectives (PEOs):

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.

About the Course

Facts about AI

AI was first coined in the year 1956 by the scientist name John McCarthy.
In 1957, the first version of a new program named as General Problem Solver (GPS) was developed and tested. This program was also developed by Newell and Simon.
Fuzzy set and logic was developed by L. Zadeh in 1960 that had the unique ability to make decisions under uncertain conditions.
ELIZA the pioneering chatbot developed by Joseph Weizenbaum at MIT holds the conversations with humans.

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.

The term "artificial intelligence" had previously been used to describe machines that mimic and display "human" cognitive skills that are associated with the human mind, such as "learning" and "problem-solving". This definition has since been rejected by major AI researchers who now describe AI in terms of rationality and acting rationally, which does not limit how intelligence can be articulated.

AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go).[2] As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[3] For instance, optical character recognition is frequently excluded from things considered to be AI,[4] having become a routine technology.

Syllabus

Module 1

Introduction to AI: history, Intelligent systems, foundation and sub area of AI, applications, current trend and development of AI. Problem solving: state space search and control strategies.

Module 2

Problem reduction and Game playing: Problem reduction, game playing, Bounded look-ahead strategy, alpha-beta pruning, Two player perfect information games.

Module 3

Logic concepts and logic Programming: propositional calculus, Propositional logic, natural deduction system, semantic tableau system, resolution refutation, predicate logic, Logic programming.

Module 4

Advanced problem solving paradigm: Planning: types of planning system, block world problem, logic based planning, Linear planning using a goal stack, Means-ends analysis, Non-linear planning strategies, learning plans.

Module 5

Knowledge Representation, Expert system

Approaches to knowledge representation, knowledge representation using semantic network, extended semantic networks for KR, Knowledge representation using Frames.

Expert system: introduction phases, architecture ES verses Traditional system.

Lesson Plan

CourseHandout-Theory-AI.pdf

Notes

Module - 1.pdf

Module-1

Module - 2.pdf

Module-2

Module - 3.pdf

Module-3

FOPL Examples Solution.pdf

FOPL Example

Module - 4.pdf

Module-4

Module - 5.pdf

Module-5

Strips for Monkeys and Banana Problem.pdf

Strips for Monkeys and Banana Problem

Assignments

Assignment-1.pdf

Assignment-1

Assignment-2.pdf

Assignment-2