315350: Ml in Robotics Syllabus for Automation & Robotics 5th Sem K Scheme MSBTE PDF

Ml in Robotics detailed Syllabus for Automation & Robotics (AO), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.

For all other MSBTE Automation & Robotics 5th Sem K Scheme Syllabus PDF, do visit MSBTE Automation & Robotics 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for ml in robotics is as follows.

Rationale

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.
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Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Validate a given predictive machine learning model .
2. Apply supervised machine learning algorithms for solving problems in robotics.
3. Use unsupervised machine learning for solving practical problems in robotics.
4. Choose artificial neural network (ANN) for robotic applications.
5. Apply machine learning in robotics.

Unit I

Basics of Machine Learning 1.1 Definition of Machine Learning (ML), need of ML 1.2 Classification of machine learning : supervised and unsupervised, semi – supervised and reinforcement 1.3 Evaluation metrics : confusion matrix, accuracy, precision, recall/sensitivity and specificity 1.4 Validation techniques : k-fold cross validation, hyperparameter tuning 1.5 Deep learning : definition, concept and classification of deep learning – artificial neural network, fuzzy logic, expert systems( only enlist, No explanation)

Suggested Learning Pedagogie
Lecture using Chalk-Board Presentations Hands-on

Unit II

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.
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Unit III

Unsupervised Machine Learning 3.1 Definition of unsupervised machine learning, types – clustering and association, applications 3.2 Working of unsupervised learning algorithms 3.3 Unsupervised learning algorithms: Types- K-means clustering, hierarchical clustering (Only key points) 3.4 Association rule learning: types-support, confidence and lift, types of algorithms- Apriori algorithm, Eclat algorithm, F-P Growth algorithm (enlist only, no explanation)

Suggested Learning Pedagogie
Lecture using Chalk-Board Presentations Hands-on Simulation

Unit IV

Overview of Artificial Neural Network 4.1 Biological neuron: structure and function 4.2 Neural networks: basics of neural networks, artificial neural networks(ANN). unit in neural networks 4.3 ANN structure: artificial neuron structure and functions 4.4 Types of ANN: single layer feed-forward and multi-layer feedforward neural networks 4.5 Back-propagation in neural network: working of forward pass and backward pass(No mathematical derivation)

Suggested Learning Pedagogie
Lecture using Chalk-Board Presentations Hands-on

Unit V

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.
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List of Experiments:

1. Implementation of confusion matrix for a given supervised machine learning model
2. * Implementation of evaluation metrics for a given predictive ML model .
3. Implementation regression benchmark for a given predictive model.
4. * Implementation of simple linear regression algorithm
5. Implementation of multiclass classification
6. *Implementation of support vector machine algorithm
7. Implementation of decision tree algorithm OR Implementation of random forest algorithm
8. *Implementation of K-means clustering
9. *Implementation of basic artificial neural network using python OR *Implementation of backpropagation neural network
10. *Implementation of ML program to pick and place operation in robotics

Self Learning

Assignment

• Prepare a powerpoint presentation on ML techniques used in robotics
• Prepare the list of various ML techniques used in various types of robots. Also write their specifications.
• Prepare a power point presentation to correlate machine learning work flow with student life
• Prepare a powerpoint presentation based on daily life activities as supervised and unsupervised machine learning.

Micro Project

• Case study: House price prediction using unsupervised ML- resources required, Literature review,python program, output
• Develop a program using Machine learning algorithm allows robots to grasp and manipulate objects with precision and dexterity. By analyzing the shape, size, and texture of objects,
• Case study: Any specific disease prediction using supervised ML-Data set collection resources required, Literature review,python program, output

Laboratory Equipment

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.
.

Learning Materials

1. Saroj Kaushik Artificial Intelligence CENGAGE Learning. ISBN-13: 978-81-315-1099-5 ISBN-10: 81315-315-1099-9
2. Munesh Chandra Trivedi A Classical Approach Intelligence to Artificial Khanna Book Publishing Co. (P) Ltd. New Delhi 978-81-9069889-4
3. Monica Bianchini, Milan Simic, Ankush Ghosh, Rabindra Nath Shaw Machine Learning for Robotics Applications Springer 978-981-16-0597-0
4. Indranath Chatterjee, Sheetal Zalte Machine Learning Applications: From Computer Vision to Robotics Wiley 978-1-394-17334-1
5. Govers, Francis X. Artificial Intelligence for Robotics: Build intelligent robots that perform human tasks using AI techniques Packt Publishing Limited ISBN : 978- 1788835442

Learning Websites

1. https://doi.org/10.1007/978-981-16-0598-7 e-book on Machine Learning for Robotics Applications
2. https://www.youtube.com/watch?v=PmxPXYtn1ew Machine learning applications
3. https://www.youtube.com/watch?v=k64wPf_WSDQ YouTube Video : The Basics of Robotics Theory: Machine learning applied to robotics
4. https://www.youtube.com/watch?v=4Rl8S7stN5A Machine learning applications
5. https://onlinecourses.nptel.ac.in/noc23_cs18/preview Introduction to Machine Learning By Prof. Balaraman Ravindran IIT Madras
6. https://onlinecourses.nptel.ac.in/noc23_ee87/preview Machine Learning And Deep Learning – Fundamentals And Applications By Prof. M. K. Bhuyan IIT Guwahati
7. https://medium.com/eni-digitalks/machine-learning-for-beginn ers-with-orange-data-mining- 0690372533b9#:~:text=How%20to%20 install%20and%20configure,ways%20to%20install%20this%20tool ML simulator software

For detail Syllabus of all other subjects of Automation & Robotics, K scheme do visit Automation & Robotics 5th Sem Syllabus for K scheme.

For all Automation & Robotics results, visit MSBTE Automation & Robotics all semester results direct links.