{"id":29327,"date":"2025-04-14T17:46:29","date_gmt":"2025-04-14T12:16:29","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/315350-ml-in-robotics-syllabus-for-automation-robotics-5th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T17:46:29","modified_gmt":"2025-04-14T12:16:29","slug":"315350-ml-in-robotics-syllabus-for-automation-robotics-5th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/315350-ml-in-robotics-syllabus-for-automation-robotics-5th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"315350: Ml in Robotics Syllabus for Automation & Robotics 5th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Ml in Robotics detailed Syllabus for Automation & Robotics (AO), K scheme PDF has been taken from the MSBTE<\/a> 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. <\/p>\n

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

Rationale<\/h4>\n

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
\"Get<\/a>.<\/p>\n

Course Outcomes:<\/h4>\n

Students will be able to achieve & demonstrate the following COs on completion of course based learning<\/p>\n

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

    Unit I<\/h4>\n

    Basics of Machine Learning 1.1\tDefinition of Machine Learning (ML), need of ML 1.2\tClassification of machine learning : supervised and unsupervised, semi – supervised and reinforcement 1.3\tEvaluation metrics : confusion matrix, accuracy, precision, recall\/sensitivity and specificity 1.4\tValidation techniques : k-fold cross validation, hyperparameter tuning 1.5\tDeep learning : definition, concept and classification of deep learning – artificial neural network, fuzzy logic, expert systems( only enlist, No explanation)\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture using Chalk-Board Presentations Hands-on\n<\/p>\n

    Unit II<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    Unit III<\/h4>\n

    Unsupervised Machine Learning 3.1\tDefinition of unsupervised machine learning, types – clustering and association, applications 3.2\tWorking of unsupervised learning algorithms 3.3\tUnsupervised learning algorithms: Types- K-means clustering, hierarchical clustering (Only key points) 3.4\tAssociation rule learning: types-support, confidence and lift, types of algorithms- Apriori algorithm, Eclat algorithm, F-P Growth algorithm (enlist only, no explanation)\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture using Chalk-Board Presentations Hands-on Simulation\n<\/p>\n

    Unit IV<\/h4>\n

    Overview of Artificial Neural Network 4.1\tBiological neuron: structure and function 4.2\tNeural networks: basics of neural networks, artificial neural networks(ANN). unit in neural networks 4.3\tANN structure: artificial neuron structure and functions 4.4\tTypes of ANN: single layer feed-forward and multi-layer feedforward neural networks 4.5\tBack-propagation in neural network: working of forward pass and backward pass(No mathematical derivation)\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture using Chalk-Board Presentations Hands-on\n<\/p>\n

    Unit V<\/h4>\n

    For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App<\/a>
    It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b>
    \"Get<\/a>.<\/p>\n

    List of Experiments:<\/h4>\n
      \n
    1. Implementation of confusion matrix for a given supervised machine learning model<\/li>\n
    2. * Implementation of evaluation metrics for a given predictive ML model .<\/li>\n
    3. Implementation regression benchmark for a given predictive model.<\/li>\n
    4. * Implementation of simple linear regression algorithm<\/li>\n
    5. Implementation of multiclass classification<\/li>\n
    6. *Implementation of support vector machine algorithm<\/li>\n
    7. Implementation of decision tree algorithm OR Implementation of random forest algorithm<\/li>\n
    8. *Implementation of K-means clustering<\/li>\n
    9. *Implementation of basic artificial neural network using python OR *Implementation of backpropagation neural network<\/li>\n
    10. *Implementation of ML program to pick and place operation in robotics<\/li>\n<\/ol>\n

      Self Learning<\/h4>\n<\/p>\n

      Assignment<\/i><\/p>\n