Machine Learning detail syllabus for Mechatronics (Mechatronics), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17MT752), 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.
For all other mechatronics 7th sem syllabus for be 2017 scheme vtu you can visit Mechatronics 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-IV subjects do refer to Professional Elective-IV. The detail syllabus for machine learning is as follows.
Course Objectives:
Students will be able to
- Gain Knowledge of Machine Learning, Decision Tree Learning, Artificial Neural Networks, Bayesian Learning, Evaluating Hypothesis.
- Understand the working methodology of Machine Learning, Decision Tree Learning, Artificial Neural Networks, Bayesian Learning, evaluating Hypothesis
Module 1
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.
Module 2
Plasticity effects: Irwin plastic zone correction. Dugdale’s approach. The shape of the plastic zone for plane stress and plane strain cases.Theplate thickness effect, nuMTrical problems.Determination of Stress intensity factors and plane strain fracture toughness: Introduction, estimation of stress intensity factors. ExperiMTntal MTthod- Plane strain fracture toughness test, The Standard test,sizerequireMTnts,etc.
Module 3
Artificial Neural Networks: Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm.
Module 4
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.
Module 5
Evaluating Hypothesis: Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypothesis, Comparing learning algorithms.
Course Outcomes:
On completion of course students will
- Have Knowledge of Machine Learning ,Decision Tree Learning , Artificial Neural Networks, Bayesian Learning, Evaluating Hypothesis.
- Understand the working methodology of Machine Learning ,Decision Tree Learning , Artificial Neural Networks, Bayesian Learning, Evaluating Hypothesis.
Text Books:
- Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.
Reference Books:
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, 2nd edition, springer series in statistics.
- Ethem Alpaydin, Introduction to machine learning, second edition, MIT press.
For detail syllabus of all other subjects of BE Mechatronics, 2017 regulation do visit Mechatronics 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.