sc JNTUH B.TECH R18 4-1 Syllabus For machine learning PDF 2022 – Cynohub


JNTUH B.TECH R18 4-1 Syllabus For machine learning PDF 2022


JNTUH B.TECH R18 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 – Well-posed learning problems, designing a learning system, Perspectives and issues in machine learning
Concept learning and the general to specific ordering – introduction, a concept learning task, concept learning as search, find-S: finding a maximally specific hypothesis, version spaces and the candidate elimination algorithm, remarks on version spaces and candidate elimination, inductive bias.
Decision Tree Learning – Introduction, decision tree representation, appropriate problems for decision tree learning, the basic decision tree learning algorithm, hypothesis space search in decision tree learning, inductive bias in decision tree learning, issues in decision tree learning.

machine learning Unit Two


Artificial Neural Networks-1– Introduction, neural network representation, appropriate problems for neural network learning, perceptions, multilayer networks and the back-propagation algorithm.
Artificial Neural Networks-2- Remarks on the Back-Propagation algorithm, An illustrative example: face recognition, advanced topics in artificial neural networks.
Evaluation Hypotheses – Motivation, estimation hypothesis accuracy, basics of sampling theory, a general approach for deriving confidence intervals, difference in error of two hypotheses, comparing learning algorithms.

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machine learning Unit Three


Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory – Introduction, probably learning an approximately correct hypothesis, sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression, radial basis functions, case-based reasoning, remarks on lazy and eager learning.

machine learning Unit Four


Genetic Algorithms – Motivation, Genetic algorithms, an illustrative example, hypothesis space search, genetic programming, models of evolution and learning, parallelizing genetic algorithms.
Learning Sets of Rules – Introduction, sequential covering algorithms, learning rule sets: summary, learning First-Order rules, learning sets of First-Order rules: FOIL, Induction as inverted deduction, inverting resolution.
Reinforcement Learning – Introduction, the learning task, Q–learning, non-deterministic, rewards and actions, temporal difference learning, generalizing from examples, relationship to dynamic programming.

machine learning Unit Five


Analytical Learning-1- Introduction, learning with perfect domain theories: PROLOG-EBG, remarks on explanation-based learning, explanation-based learning of search control knowledge.
Analytical Learning-2-Using prior knowledge to alter the search objective, using prior knowledge to augment search operators.
Combining Inductive and Analytical Learning – Motivation, inductive-analytical approaches to learning, using prior knowledge to initialize the hypothesis.

machine learning Course Objectives

This course explains machine learning techniques such as decision tree learning, Bayesian learning etc.
To understand computational learning theory.
To study the pattern comparison techniques.

machine learning Course Outcomes

Understand the concepts of computational intelligence like machine learning
Ability to get the skill to apply machine learning techniques to address the real time problems in different areas
Understand the Neural Networks and its usage in machine learning application.

machine learning Text Books

1. Machine Learning – Tom M. Mitchell, – MGH

machine learning Reference Books

1. Machine Learning: An Algorithmic Perspective, Stephen Marshland, Taylor & Francis

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 JNTUH B.Tech R18 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

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