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
I
MA101BS
Mathematics – I
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I
AP102BS
Applied Physics
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I
CS103ES
Programming for Problem Solving
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I
ME104ES
Engineering Graphics
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I
AP105BS
Applied Physics Lab
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I
CS106ES
Programming for Problem Solving Lab
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I
MC109ES
Environmental Science
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II
MA201BS
Mathematics – II
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II
CH202BS
Chemistry
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II
EE203ES
Basic Electrical Engineering
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II
ME205ES
Engineering Workshop
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II
EN205HS
English
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II
CH206BS
Engineering Chemistry Lab
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II
EN207HS
English Language and Communication Skills Lab
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II
EE208ES
Basic Electrical Engineering Lab
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II-I
CS310PC
Discrete Mathematics
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II-I
CS302PC
Data Structures
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II-I
MA313BS
Mathematical and Statistical Foundations
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II-I
CS304PC
Computer Organization and Architecture
II-I
CS311PC
Python Programming
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II-I
SM306MS
Business Economics & Financial Analysis
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II-I
CS307PC
Data Structures Lab
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II-I
CS312PC
Python Programming Lab
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II-I
MC309
Gender Sensitization Lab
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II-II
CS416PC
Formal Language and Automata Theory
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II-II
CS417PC
Software Engineering
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II-II
CS403PC
Operating Systems
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II-II
CS404PC
Database Management Systems
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II-II
CS412PC
Object Oriented Programming using Java
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II-II
CS406PC
Operating Systems Lab
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II-II
CS407PC
Database Management Systems Lab
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II-II
CS408PC
Java Programming Lab
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II-II
MC409
Constitution of India
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III-I
Design and Analysis of Algorithms
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III-I
Machine Learning
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III-I
Computer Networks
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III-I
Compiler Design
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III-I
Graph Theory (PE1)
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III-I
Introduction to Data Science(PE1)
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III-I
Web Programming(PE1)
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III-I
Image Processing(PE1)
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III-I
Computer Graphics(PE1)
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III-I
Software Testing Methodologies(PE2)
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III-I
Information Retrieval Systems(PE2)
III-I
Pattern Recognition(PE2)
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III-I
Computer Vision and Robotics(PE2)
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III-I
Data Warehousing and Business Intelligence(PE2)
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III-I
Machine Learning Lab
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III-I
Computer Networks Lab
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III-I
Advanced Communication Skills Lab
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III-I
Intellectual Property Rights
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III-II
Artificial Intelligence
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III-II
DevOps
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III-II
Natural Language Processing
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III-II
Internet of Things(PE3)
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III-II
Data Mining(PE3)
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III-II
Scripting Languages(PE3)
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III-II
Mobile Application Development(PE3)
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III-II
Cryptography and Network Security(PE3)
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III-II
Artificial Intelligence and Natural Language
Processing Lab
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