315330: Ai & Ml Algorithm Syllabus for Artificial Intelligence 5th Sem K Scheme MSBTE PDF

Ai & Ml Algorithm detailed Syllabus for Artificial Intelligence (AI), 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 Artificial Intelligence 5th Sem K Scheme Syllabus PDF, do visit MSBTE Artificial Intelligence 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for ai & ml algorithm is as follows.

Rationale

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Course Outcomes:

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

1. Implement relevant search algorithms as applicable to Artificial Intelligence.
2. Apply method for knowledge representation to make informed decisions for various applications.
3. Analyze different forms of data with respect to different phases of Machine Learning.
4. Create data model for Machine Learning Algorithms.
5. Classify the data by performing different Regression Techniques.

Unit I

Basics of AI and Problem Solving Techniques 1.1 Basic Definition and Terminology: Foundation and Evaluation of AI, Scope of AI, Components of AI, Types of AI, Application of AI 1.2 Intelligent Agent in AI: Types of AI agent, Concept of Rationality, Nature of environment, Structure of agents, Turing Test in AI 1.3 Search Algorithms in Artificial Intelligence: Properties of Search Algorithms, Types of Search Algorithms 1.4 Heuristic 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

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Flipped Classroom

Unit II

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Unit III

Introduction to ML 3.1 History and Evaluation of ML, AI vs ML 3.2 Machine Learning Life Cycle: Gathering data, Data Preparation, Data Wrangling, Data Analysis, Train Model, Test Model, Deployment 3.3 Different forms of Data: Data Mining, Data Analytics, Statistics Data, Statistics vs. Data Mining, Data Analytics vs Data Science 3.4 Dataset for ML: Training Dataset, Testing Datasets, Training vs Testing 3.5 Data Cleaning: Missing Data, Outliers

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit IV

Types of Learning 4.1 Types of Learning: Supervised, Unsupervised, SemiSupervised Learning 4.2 Supervised 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 Unsupervised Learning: Introduction of different types of Unsupervised Learning Algorithm: K-means clustering, KNN (k-Nearest Neighbors), Hierarchical Clustering, Neural Networks 4.4 Model evaluation: Introduction of Cross-validation, benefits of cross-validation, Positive and Negative class cross-validation

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations Demonstration

Unit V

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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List of Experiments:

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

Self Learning

Micro Project

• Develop a micro project for Movie Recommendation System: Use a dataset like the MovieLens dataset, preprocess the data (split into training and test sets),train a collaborative filtering model and generate and evaluate recommendations for users.
• Develop a micro project for Simple Chatbot: define a set of intents and responses and train a dataset to classify user inputs.
• Develop a micro project for Spam Email Classifier in which collect a dataset of labelled emails (spam or not spam), pre-process the text data (remove stop words, tokenize, etc.)
• Case study on Natural Language Generation (NLG) for E-commerce Product Descriptions

Other

• Complete the course Artificial Intelligence and Machine Learning on Infosys Springboard.
• Develop a code for given problem suggested by teacher.

Assignment

• Can Artificial Intelligence replace human Intelligence? Justify it
• Describe role of artificial intelligence in banking.
• Compare OpenAI and ChatGPT.
• Identify & List out the equipment / machine available in your Institute where AI technology is used. Describe the role of AI in that equipment.

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.
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Learning Materials

1. Stuart Russell and Peter Norvig, Editors Artificial Intelligence A modern Approach Third edition Pearson Education, Inc ISBN-13: 978-0-13604259-4 ISBN-10: 0-13-604259-7
2. Dr. Jeeva Jose Introduction to Machine Learning with Python Khanna Book Publishing Co.(P) Ltd. ISBN 9789389139068 ISBN 9789389139068
3. Dipanjan Sarkar Raghav Bali Tushar Sharma Practical Machine Learning with Python A ProblemSolver’s Guide to Building Real-World Intelligent Systems Apress publication ISBN-13 (pbk): 978-14842-3206-4 ISBN-13 (electronic): 978-1-4842-3207-1
4. Andreas C. Müller & Sarah Guido Introduction to Machine Learning with Python O’Reilly Media, Inc ISBN 9352134575 ISBN 9789352134571
5. Manaramjan Pradhan, U Dinesh Kumar Machine Learning using Python Wiley India ISBN 978-81-265-7990-7 ISBN 9 788126 579907

Learning Websites

1. https://www.python.org/downloads/ Python IDE download
2. https://www.pdfdrive.com/machine-learning-step-by-step-guide -to-implement-machine-learning-algorithms-with-python-d15832 4853.html AI and ML E-Books
3. https://www.geeksforgeeks.org/how-to-install-python-pycharm-on-windows Guidelines for Installation of python
4. https://stackabuse.com/courses/graphs-in-python-theory-and-i mplementation/lessons/a-star-search-algorithm A* algorithm
5. https://www.javatpoint.com/turing-test-in-ai Turing test
6. https://www.v7labs.com/blog/best-free-datasets-for-machine-l earning Datasets
7. https://www.geeksforgeeks.org/how-to-split-a-dataset-into-tr ain-and-test-sets-using-python Training and Testing Dataset
8. https://towardsdatascience.com/email-spam-detection-1-2-b0e0 6a5c0472 Filtering Dataset

For detail Syllabus of all other subjects of Artificial Intelligence, K scheme do visit Artificial Intelligence 5th Sem Syllabus for K scheme.

For all Artificial Intelligence results, visit MSBTE Artificial Intelligence all semester results direct links.