sc JNTU-K B.TECH R19 4-1 Syllabus For Machine learning PDF 2022 – Cynohub


JNTU-K B.TECH R19 4-1 Syllabus For Machine learning PDF 2022


JNTU-K B.TECH R19 4-1 Syllabus For Machine learning PDF 2022

Get Complete Lecture Notes for Machine learning on Cynohub APP

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

Machine learning Unit One


Introduction: Definition of learning systems, Goals and applications of machine learning, Aspects of developing a learning system: training data, concept representation, function approximation.

Inductive Classification: The concept learning task, Concept learning as search through a hypothesis space, General-to-specific ordering of hypotheses, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Learning conjunctive concepts, The importance of inductive bias

Machine learning Unit Two

Decision Tree

Decision Tree Learning: Representing concepts as decision trees, Recursive induction of decision trees, Picking the best splitting attribute: entropy and information gain, Searching for simple trees and computational complexity, Occam’s razor, Overfitting, noisy data, and pruning. Experimental Evaluation of Learning Algorithms: Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing.

Get Complete Lecture Notes for Machine learning on Cynohub APP

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Machine learning Unit Three

Computational Learning Theory

Computational Learning Theory: Models of learnability: learning in the limit; probably approximately correct (PAC) learning. Sample complexity for infinite hypothesis spaces, Vapnik-Chervonenkis dimension.

Rule Learning: Propositional and First-Order, Translating decision trees into rules, Heuristic rule induction using separate and conquer and information gain, First-order Horn-clause induction (Inductive Logic Programming) and Foil, Learning recursive rules, Inverse resolution, Golem, and Progol.

Machine learning Unit Four

Artificial Neural Network

Artificial Neural Networks: Neurons and biological motivation, Linear threshold units. Perceptrons: representational limitation and gradient descent training, Multilayer networks and backpropagation, Hidden layers and constructing intermediate, distributed representations. Overfitting, learning network structure, recurrent networks.

Support Vector Machines: Maximum margin linear separators. Quadractic programming solution to finding maximum margin separators. Kernels for learning non-linear functions.

Machine learning Unit Five

Bayesian Learning

Bayesian Learning: Probability theory and Bayes rule. Naive Bayes learning algorithm. Parameter smoothing. Generative vs. discriminative training. Logisitic regression. Bayes nets and Markov nets for representing dependencies.

Instance-Based Learning: Constructing explicit generalizations versus comparing to past specific examples. k-Nearest-neighbor algorithm. Case-based learning.

Machine learning Course Objectives

The course is introduced for students to

 Gain knowledge about basic concepts of Machine Learning

 Study about different learning algorithms

 Learn about of evaluation of learning algorithms

 Learn about Dimensionality reduction

Machine learning Course Outcomes

 Identify machine learning techniques suitable for a given problem

 Solve the problems using various machine learning techniques

 Apply Dimensionality reduction techniques

 Design application using machine learning techniques

Machine learning Text Books

1) T.M. Mitchell, “Machine Learning”, McGraw-Hill, 1997.

2) Machine Learning, Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das, Pearson, 2019.

Machine learning Reference Books

1) Ethern Alpaydin, “Introduction to Machine Learning”, MIT Press, 2004.

2) Stephen Marsland, “Machine Learning -An Algorithmic Perspective”, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.

3) Andreas C. Müller and Sarah Guido “Introduction to Machine Learning with Python: A Guide for Data Scientists”, Oreilly.

Scoring Marks in Machine learning

Scoring a really good grade in Machine learning 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 Machine learning. There are a lot of reasons for getting a bad score in your Machine learning exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTU-K B.Tech R19 Machine learning 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.

Get Complete Lecture Notes for Machine learning on Cynohub APP

Download the APP Now! ( Click Here )

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  1. yampalaku Lakshmi


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