{"id":7060,"date":"2019-12-14T16:50:19","date_gmt":"2019-12-14T16:50:19","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/machine-learning-is-7th-sem-syllabus-for-vtu-be-2017-scheme\/"},"modified":"2019-12-14T16:50:19","modified_gmt":"2019-12-14T16:50:19","slug":"machine-learning-is-7th-sem-syllabus-for-vtu-be-2017-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/machine-learning-is-7th-sem-syllabus-for-vtu-be-2017-scheme\/","title":{"rendered":"Machine Learning IS 7th Sem Syllabus for VTU BE 2017 Scheme"},"content":{"rendered":"

Machine Learning detail syllabus for Information Science Engineering (Is), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17CS73), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other is 7th sem syllabus for be 2017 scheme vtu you can visit IS 7th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. The detail syllabus for machine learning is as follows.<\/p>\n

Module 1\t\t\t\t<\/h4>\n

Introduction: Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias. Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7\n<\/p>\n

Module 2
\nFor complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3
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Artificial Neural Networks: Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm. Text book 1, Sections: 4.1 – 4.6\n<\/p>\n

Module 4
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Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12\n<\/p>\n

Module 5
\nFor complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to\t\t\t\t<\/p>\n