JNTUH CSE-AIML MACHINE LEARNING SYLLABUS

JNTUH CSE-AIML MACHINE LEARNING SYLLABUS

Prerequisites:
1. Data Structures
2. Knowledge on statistical methods
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.
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.
UNIT – I
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.
UNIT – II
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.
UNIT – III
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.
UNIT- IV
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.
UNIT – V
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.
TEXT BOOK:
1. Machine Learning – Tom M. Mitchell, – MGH
REFERENCE BOOK:
2. Machine Learning: An Algorithmic Perspective, Stephen Marshland, Taylor & Francis

CSE-AIML

SEMESTER SUBJECT CODE SUBJECT Lession Plan Lecturer Notes & Question Bank SYLLABUS ABOUT TUTOR
I MA101BS Mathematics – I Click Here
I AP102BS Applied Physics Click Here
I CS103ES Programming for Problem Solving Click Here
I ME104ES Engineering Graphics Click Here
I AP105BS Applied Physics Lab Click Here
I CS106ES Programming for Problem Solving Lab Click Here
I MC109ES Environmental Science Click Here
II MA201BS Mathematics – II Click Here
II CH202BS Chemistry Click Here
II EE203ES Basic Electrical Engineering Click Here
II ME205ES Engineering Workshop Click Here
II EN205HS English Click Here
II CH206BS Engineering Chemistry Lab Click Here
II EN207HS English Language and Communication Skills Lab Click Here
II EE208ES Basic Electrical Engineering Lab Click Here
II-I CS310PC Discrete Mathematics Click Here
II-I CS302PC Data Structures Click Here
II-I MA313BS Mathematical and Statistical Foundations Click Here
II-I CS304PC Computer Organization and Architecture Click Here
II-I CS311PC Python Programming Click Here
II-I SM306MS Business Economics & Financial Analysis Click Here
II-I CS307PC Data Structures Lab Click Here
II-I CS312PC Python Programming Lab Click Here
II-I MC309 Gender Sensitization Lab Click Here
II-II CS416PC Formal Language and Automata Theory Click Here
II-II CS417PC Software Engineering Click Here
II-II CS403PC Operating Systems Click Here
II-II CS404PC Database Management Systems Click Here
II-II CS412PC Object Oriented Programming using Java Click Here
II-II CS406PC Operating Systems Lab Click Here
II-II CS407PC Database Management Systems Lab Click Here
II-II CS408PC Java Programming Lab Click Here
II-II MC409 Constitution of India Click Here
III-I Design and Analysis of Algorithms Click Here
III-I Machine Learning Click Here
III-I Computer Networks Click Here
III-I Compiler Design Click Here
III-I Graph Theory (PE1) Click Here
III-I Introduction to Data Science(PE1) Click Here
III-I Web Programming(PE1) Click Here
III-I Image Processing(PE1) Click Here
III-I Computer Graphics(PE1) Click Here
III-I Software Testing Methodologies(PE2) Click Here
III-I Information Retrieval Systems(PE2) VIJAYANAND S
III-I Pattern Recognition(PE2) Click Here
III-I Computer Vision and Robotics(PE2) Click Here Click Here
III-I Data Warehousing and Business Intelligence(PE2) Click Here
III-I Machine Learning Lab Click Here
III-I Computer Networks Lab Click Here
III-I Advanced Communication Skills Lab Click Here
III-I Intellectual Property Rights Click Here
III-II Artificial Intelligence Click Here Click Here
III-II DevOps Click Here Click Here
III-II Natural Language Processing Click Here Click Here
III-II Internet of Things(PE3) Click Here Click Here
III-II Data Mining(PE3) Click Here Click Here
III-II Scripting Languages(PE3) Click Here Click Here
III-II Mobile Application Development(PE3) Click Here Click Here
III-II Cryptography and Network Security(PE3) Click Here Click Here
III-II Artificial Intelligence and Natural Language Processing Lab Click Here Click Here
III-II DevOps Lab Click Here Click Here
IV-I Neural Networks & Deep Learning Click Here Click Here
IV-I Reinforcement Learning Click Here Click Here
IV-I Quantum Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Expert Systems(PE4) Click Here Click Here
IV-I Cloud Computing(PE4) Click Here Click Here
IV-I Game Theory(PE4) Click Here Click Here
IV-I Mobile Computing(PE4) Click Here Click Here
IV-I Social Network Analysis(PE5) Click Here Click Here
IV-I Federated Machine Learning(PE5) Click Here Click Here
IV-I Augmented Reality & Virtual Reality(PE5) Click Here Click Here
IV-I Web Security(PE5) Click Here Click Here
IV-I Ad-hoc & Sensor Networks Click Here Click Here
IV-I Deep Learning Lab Click Here Click Here
IV-II Organizational Behaviour Click Here Click Here
IV-II Speech and Video Processing(PE6) Click Here Click Here
IV-II Robotics Process Automation(PE6) Click Here Click Here
IV-II Randomized Algorithms(PE6) Click Here Click Here
IV-II Cognitive Computing(PE6) Click Here Click Here
IV-II Semantic Web(PE6) Click Here Click Here

Add Comment

Your email address will not be published. Required fields are marked *

Style switcher RESET
Body styles
Color settings
Link color
Menu color
User color
Background pattern
Background image