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JNTU-K B.TECH R-19 4-1 Syllabus For Artifical intelligence and machine learningprofessional elective -iii PDF 2022

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JNTU-K B.TECH R-19 4-1 Syllabus For Artifical intelligence and machine learningprofessional elective -iii PDF 2022

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You will be able to find information about Artifical intelligence and machine learningprofessional elective -iii 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 Artifical intelligence and machine learningprofessional elective -iii after reading this blog. Artifical intelligence and machine learningprofessional elective -iii has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Artifical intelligence and machine learningprofessional elective -iii 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 Artifical intelligence and machine learningprofessional elective -iii are mentioned below in detail. If you are having a hard time understanding Artifical intelligence and machine learningprofessional elective -iii 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.

Artifical intelligence and machine learningprofessional elective -iii Unit One

INTRODUCTION TO MACHINE LEARNING / ARTIFICIAL INTELLIGENCE

Artificial Intelligence Foundation, Big questions of Artificial Intelligence, history of Artificial Intelligence, latest advancements

Artifical intelligence and machine learningprofessional elective -iii Unit Two

UNIT 2

MACHINE LEARNING: Linear Regression -Learn to implement linear regression and predict continuous data values, Naïve Bayes and Logistic regression -Understand how supervised learning is used for classification, Clustering -Learn how to create segments based on similarities using K-Means and Hierarchical clustering, Support vector machines -Learn to classify data points using support vectors, decision trees -Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them
NATURAL LANGUAGE PROCESSING:Basics of text processing, lexical processing -Learn to extract features from unstructured text and build machine learning models on text data, syntax and semantics -Conduct sentiment analysis, learn to parse English sentences and extract meaning from them

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Artifical intelligence and machine learningprofessional elective -iii Unit Three

DEEP LEARNING & NEURAL NETWORKS

Information flow in neural networks -Understand the components and structure of artificial neural networks, Training a neural network -Learn the latest techniques used to train highly complex neural networks, Convolutional neural networks -Use CNN’s to solve complex image classification problems, Recurrent neural networks -Study LSTMs and RNN’s applications in text analytics, Creating and deploying networks using Tensor Flow and keras -Build and deploy your own deep neural networks on a website, learn to use Tensor Flow API and keras

Artifical intelligence and machine learningprofessional elective -iii Unit Four

GRAPHICAL MODELS

Introduction to Bayesian methods, Graphical models -Study probabilistic way of modelling systems -Markov properties, Factor Graphs and Bayesian belief networks, Learning and Inference -Learn how graphics models are used for supervised and unsupervised learning.

Artifical intelligence and machine learningprofessional elective -iii Unit Five

REINFORCEMENT LEARNING

Introductionto RL, understand how machines can be programmed to learn by themselves, Exact methods -Learn the math behind Exact Statistics -Dynamic Programming, Monte Carlo methods, Temporal Dierence Learning, Approximate Methods -Learn policy gradient methods and their applications in learning

Artifical intelligence and machine learningprofessional elective -iii Course Objectives

The main objective of this course is:
To familiarize students with basic concepts, theories and advancements in ML and AI and help them in understanding the mathematics behind algorithms and apply them in real world scenarios.

Artifical intelligence and machine learningprofessional elective -iii Course Outcomes

At the end of this course the student will be able to:
Understand machine learning concepts and range of problems that can be handled by machine learning.
Apply the machine learning concepts in real life problems.
Understand artificial neural networks concept and apply techniques to train the neural networks.
Understand how graphical models are used for supervised and unsupervised learning.
Understand Reinforcement Learning concept and applications.
Modify the algorithms based on need.

Artifical intelligence and machine learningprofessional elective -iii Text Books

1.Machine Learning, by Tom M Mitchell, Indian Edition, McGraw Hill
2.Deep Learning by Good fellow, Bengio, Courville. The MIT Press, 2016

Artifical intelligence and machine learningprofessional elective -iii Reference Books

1.Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartzand Shai Ben-David, 1stEdition, Cambridge University Press
2.Artificial Intelligence -A Modern Approach by Stuart Russell & Peter Norvig, Prentice Hall

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Information about JNTU-K B.Tech R-19 Artifical intelligence and machine learningprofessional elective -iii 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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