5th Sem, RP

5301: Introduction To Machine Learning Syllabus for Robotics Process Automation 5th Sem 2021 Revision SITTTR

Introduction To Machine Learning detailed syllabus for Robotics Process Automation (RP) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Robotics Process Automation students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Robotics Process Automation 5th Sem scheme and its subjects, do visit Robotics Process Automation (RP) 5th Sem 2021 revision scheme. The detailed syllabus of introduction to machine learning is as follows.

Course Objectives:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Course Outcomes:

On completion of the course student will be able to:

  1. Differentiate various learning approaches, and to interpret the concepts of supervised learning
  2. Compare the different dimensionality reduction techniques
  3. Demonstrate the basic concepts of classification
  4. Study neural network and markov processes

Module 1:

Introduction to Machine Learning, Examples of Machine Learning applications – Learning associations, Classification, Regression, Unsupervised Learning, Reinforcement Learning. Supervised learning- Input representation, Hypothesis class, Version space, Vapnik-Chervonenkis (VC) Dimension

Module 2:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Module 3:

Classification- Cross validation and re-sampling methods- K- fold cross validation, Boot strapping, Measuring classifier performance- Precision, recall, ROC curves. Bayes Theorem, Bayesian classifier, Maximum Likelihood estimation Regression

Module 4:

Neural Networks- The Perceptron, Activation Functions. Support Vector Machine . Discrete Markov Processes, Hidden Markov models, Three basic problems of HMMs-Evaluation problem, Unsupervised Learning – Clustering Methods – K-means, Expectation-Maximization Algorithm.

Reference Books:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

For detailed syllabus of all other subjects of Robotics Process Automation (RP), 2021 revision curriculum do visit Robotics Process Automation 5th Sem subject syllabuses for 2021 revision.

To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..

To see the results of Robotics Process Automation (RP) of diploma 2021 revision curriculum do visit SITTTR diploma Robotics Process Automation (RP) results..

For all Robotics Process Automation academic calendars, visit Robotics Process Automation all semesters academic calendar direct link.

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