[PDF] CS8082 Machine Learning Techniques (MLT) Books, Lecture Notes, 2marks with answers, Important Part B 13marks Questions, Question Bank & Syllabus

Download CS8082 Machine Learning Techniques (MLT) Books Lecture Notes Syllabus Part A 2 marks with answers CS8082 Machine Learning Techniques (MLT) Important Part B 13 marks, Direct 16 Mark Questions and Part C 15 marks Questions, PDF Books, Question Bank with answers Key, CS8082 Machine Learning Techniques (MLT) Syllabus & Anna University CS8082 Machine Learning Techniques (MLT) Question Papers Collection, Electronics & Communication Engineering Regulation 2017 Study Materials (Notes).

Download link is provided and students can download the Anna University CS8082 Machine Learning Techniques (MLT) Syllabus Question bank Lecture Notes Part A 2 marks with answers Part B 13 marks and Part C 15 marks Question Bank with an answer, All the materials are listed below for the students to make use of it and score good (maximum) marks with our study materials.

“CS8082 Machine Learning Techniques (MLT) Notes, Lecture Notes, Previous Years Question Papers”

“CS8082 Machine Learning Techniques (MLT) Important 13 marks & 15 marks Questions with Answers”

“CS8082 Machine Learning Techniques (MLT) Important 2 marks & 16 marks Questions with Answers”

“CS8082 Machine Learning Techniques (MLT) Important Part A & Part B Questions”

“CS8082 Machine Learning Techniques (MLT) Syllabus, Local Author Books, Question Banks”

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CS8082 Machine Learning Techniques (MLT) Part A & Part B Important Questions with Answers

CS8082 Machine Learning Techniques (MLT) Syllabus

UNIT I INTRODUCTION

Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate Eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search.

UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS

Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evaluation and Learning.

UNIT III BAYESIAN AND COMPUTATIONAL LEARNING

Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.

UNIT IV INSTANT BASED LEARNING

K- Nearest Neighbour Learning – Locally weighted Regression – Radial Bases Functions – Case Based Learning.

UNIT V ADVANCED LEARNING

Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning.

Kindly Note: They different collections of CS8082 Machine Learning Techniques (MLT) Study materials are listed below. Each collection is different from others, Based on the candidate’s (your) knowledge & requirement choose the suitable material for your preparations.

• Regulation 2017 CS8082 Machine Learning Techniques (MLT) Syllabus – DOWNLOAD

CS8082 Machine Learning Techniques (MLT) Important Questions

• Important Questions Collection 1 – DOWNLOAD

CS8082 Machine Learning Techniques (MLT) Question Papers Collections

• Last 5 Years Anna University Question Papers Collection – DOWNLOAD

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