{"id":29289,"date":"2025-04-14T17:44:53","date_gmt":"2025-04-14T12:14:53","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/315330-ai-ml-algorithm-syllabus-for-ai-ml-5th-sem-k-scheme-msbte-pdf\/"},"modified":"2025-04-14T17:44:53","modified_gmt":"2025-04-14T12:14:53","slug":"315330-ai-ml-algorithm-syllabus-for-ai-ml-5th-sem-k-scheme-msbte-pdf","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/315330-ai-ml-algorithm-syllabus-for-ai-ml-5th-sem-k-scheme-msbte-pdf\/","title":{"rendered":"315330: Ai & Ml Algorithm Syllabus for AI&ML 5th Sem K Scheme MSBTE PDF"},"content":{"rendered":"

Ai & Ml Algorithm detailed Syllabus for AI&ML (AN), 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 AI&ML 5th Sem K Scheme Syllabus PDF, do visit MSBTE AI&ML 5th Sem K Scheme Syllabus PDF Subjects<\/a>. The detailed Syllabus for ai & ml algorithm 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. Implement relevant search algorithms as applicable to Artificial Intelligence.<\/li>\n
  2. Apply method for knowledge representation to make informed decisions for various applications.<\/li>\n
  3. Analyze different forms of data with respect to different phases of Machine Learning.<\/li>\n
  4. Create data model for Machine Learning Algorithms.<\/li>\n
  5. Classify the data by performing different Regression Techniques.<\/li>\n<\/ol>\n

    Unit I<\/h4>\n

    Basics of AI and Problem Solving Techniques 1.1\tBasic Definition and Terminology: Foundation and Evaluation of AI, Scope of AI, Components of AI, Types of AI, Application of AI 1.2\tIntelligent Agent in AI: Types of AI agent, Concept of Rationality, Nature of environment, Structure of agents, Turing Test in AI 1.3\tSearch Algorithms in Artificial Intelligence: Properties of Search Algorithms, Types of Search Algorithms 1.4\tHeuristic Search Techniques: Generate-and-Test; Hill Climbing. Properties of A* algorithm, Depth-First Search, Best-First Search, Greedy Best-First, Problem Reduction 1.5 Beyond Classical Search: Local search algorithms and optimization problem, Local search in continuous spaces, Searching with nondeterministic action and partial observation, Online search agent and unknown environments\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations Flipped Classroom\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

    Introduction to ML 3.1\tHistory and Evaluation of ML, AI vs ML 3.2\tMachine Learning Life Cycle: Gathering data, Data Preparation, Data Wrangling, Data Analysis, Train Model, Test Model, Deployment 3.3\tDifferent forms of Data: Data Mining, Data Analytics, Statistics Data, Statistics vs. Data Mining, Data Analytics vs Data Science 3.4\tDataset for ML: Training Dataset, Testing Datasets, Training vs Testing 3.5\tData Cleaning: Missing Data, Outliers\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations Demonstration\n<\/p>\n

    Unit IV<\/h4>\n

    Types of Learning 4.1\tTypes of Learning: Supervised, Unsupervised, SemiSupervised Learning 4.2\tSupervised Learning: Learning a Class from Examples, Introduction of different types of Supervised Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Tree, K – Nearest Neighbors, Random Forest 4.3\tUnsupervised Learning: Introduction of different types of Unsupervised Learning Algorithm: K-means clustering, KNN (k-Nearest Neighbors), Hierarchical Clustering, Neural Networks 4.4\tModel evaluation: Introduction of Cross-validation, benefits of cross-validation, Positive and Negative class cross-validation\n<\/p>\n

    Suggested Learning Pedagogie<\/i>
    \nLecture Using Chalk-Board Presentations Demonstration\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. * Install given Python IDE software and Python “scikit learn” for ML\t2<\/li>\n
    2. Write program to Implement Breadth First Search Algorithm (Uninformed) in Python\t2<\/li>\n
    3. *Write program to implement Depth First Search Algorithm (Uninformed) in Python\t2<\/li>\n
    4. Write program to implement Greedy Best-First (Informed Type) Search Algorithm in python\t4<\/li>\n
    5. * Write program to implement A* search (Informed Type) Algorithm in Python\t2<\/li>\n
    6. * Write program to implement Bayes’ Theorem\t4<\/li>\n
    7. Analyze the given Case study: How Turing test is performed between Responder and an Interrogator?\t2<\/li>\n
    8. * Explore different dataset finders e.g. Google Dataset Search, Kaggle\t2<\/li>\n
    9. * Write program in python to split any dataset into train and tests sets\t4<\/li>\n
    10. Analyze E-mail spam and non-spam filtering using Machine Learning through case study\t4<\/li>\n
    11. *Create and display a Decision Tree on given dataset\t2<\/li>\n
    12. Write program to implement K-means Algorithm\t2<\/li>\n
    13. * Write program to calculate cross validation score for any Dataset like IRIS\t2<\/li>\n
    14. *Write program to implement Simple Linear Regression using Python\t2<\/li>\n
    15. Write program to implement Multiple Linear Regression using Python\t2<\/li>\n
    16. *Write program to create confusion matrix to calculate different measures to quantify the quality of the model\t2<\/li>\n<\/ol>\n

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

      Micro Project<\/i><\/p>\n