JNTU-K B.TECH R19 3-1 Syllabus For Artificial intelligence PDF 2022


JNTU-K B.TECH R19 3-1 Syllabus For Artificial intelligence PDF 2022

Get Complete Lecture Notes for Artificial intelligence on Cynohub APP

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You will be able to find information about Artificial intelligence along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Artificial intelligence after reading this blog. Artificial intelligence has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Artificial intelligence can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Artificial intelligence are mentioned below in detail. If you are having a hard time understanding Artificial intelligence or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Artificial intelligence Unit One


Introduction, history, intelligent systems, foundations of AI, applications, tic-tac-toe game playing, development of AI languages, current trends.

Artificial intelligence Unit Two

Problem solving

Problem solving: state-space search and control strategies: Introduction, general problem solving, characteristics of problem, exhaustive searches, heuristic search techniques, iterative deepening A*, constraint satisfaction.Problem reduction and game playing: Introduction, problem reduction, game playing, alpha beta pruning, two-player perfect information games.

Get Complete Lecture Notes for Artificial intelligence on Cynohub APP

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Artificial intelligence Unit Three

Logic concepts

Logic concepts:Introduction, propositional calculus, proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in proportional logic, predicate logic.

Artificial intelligence Unit Four

Knowledge representation

Knowledge representation: Introduction, approaches to knowledge representation, knowledge representation using semantic network, extended semantic networks for KR, knowledge representation using frames.Advanced knowledge representation techniques: Introduction, conceptual dependency theory, script structure, CYC theory, case grammars, semantic web

Artificial intelligence Unit Five

Expert system and applications

Expert system and applications:Introduction phases in building expert systems, expert system versus traditional systemsUncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty factor theory, dempster-shafer theoryFuzzy sets and fuzzy logic:Introduction, fuzzy sets, fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.

Artificial intelligence Course Objectives

To have a basic proficiency in a traditional AI language including an ability to write simple to intermediate programs and an ability to understand code written in thatlanguageTo have an understanding of the basic issues of knowledge representation and blind and heuristic search, as well as an understanding of other topics such as minimax, resolution, etc. that play an important role in AI programsTo have a basic understanding of some of the more advanced topics of AI such as learning, natural language processing, agents and robotics, expert systems, and planning

Artificial intelligence Course Outcomes

Outlineproblems that are amenable to solution by AI methods, and which AI methods maybe suited to solving a given problemApplythe language/framework of different AI methodsfor a given problemImplement basic AI algorithms-standard search algorithms or dynamic programmingDesign and carry out an empirical evaluation of different algorithms on problem formalization, and state the conclusions that the evaluation supports

Artificial intelligence Text Books

1)Artificial Intelligence-Saroj Kaushik, CENGAGE Learning2)Artificial intelligence, A modern Approach , 2nded, Stuart Russel, Peter Norvig, PEA

Artificial intelligence Reference Books

1)Artificial Intelligence-Deepak Khemani, TMH, 20132)Introduction to Artificial Intelligence, Patterson, PHI3)Atificial intelligence, structures and Strategies for Complex problem solving, -George F Lugar, 5thed, PEA

Scoring Marks in Artificial intelligence

Scoring a really good grade in Artificial intelligence is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your examinations . This video will also inform students on how to score high grades in Artificial intelligence. There are a lot of reasons for getting a bad score in your Artificial intelligence exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTU-K B.Tech R19 Artificial intelligence was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

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