Machine Learning Techniques C&C 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective II) detail syllabus for Computer & Communication Engineering (C&C), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (CS8082), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other c&c 7th sem syllabus for be 2017 regulation anna univ you can visit C&C 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective II subjects do refer to Professional Elective II. The detail syllabus for machine learning techniques is as follows.
Course Objective:
- To understand the need for machine learning for various problem solving
- To study the various supervised, semi-supervised and unsupervised learning algorithms in
- To learn the new approaches in machine learning
- To design appropriate machine learning algorithms for problem solving
machine learning
Unit I
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
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 – Naive Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.
Unit IV
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
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
Course Outcome:
At the end of the course, the students will be able to
- Differentiate between supervised, unsupervised, semi-supervised machine learning approaches
- Apply specific supervised or unsupervised machine learning algorithm for a particular problem
- Analyse and suggest the appropriate machine learning approach for the various types of problem
- Design and make modifications to existing machine learning algorithms to suit an individual application
- Provide useful case studies on the advanced machine learning algorithms
Text Books:
- Tom M. Mitchell, Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
References:
- Ethem Alpaydin, Introduction to Machine Learning (Adaptive Computation and
- Stephen Marsland, Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
Machine Learning), The MIT Press 2004.
For detail syllabus of all other subjects of BE C&C, 2017 regulation do visit C&C 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.