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INTRODUCTION TO AI AND ML (21ME482)

INTRODUCTION TO AI AND ML

Course Code 21ME482 
CIE Marks 50
Teaching Hours/Week (L:T:P: S) 0:2:0:0 
SEE Marks 50
Total Hours of Pedagogy 30 
Total Marks 100
Credits 01 
Exam Hours 01


Module-1

Introduction to AI: Introduction, The Turing Test Approach, Cognitive Modeling Approach, Laws of thought Approach, Rational agent Approach, AI Methods and tools, Foundations of Artificial Intelligence, Goals of AI, Performing Natural Language Processing using Email Filters in Gmail, Performing Natural Language Generation using Smart replies in Gmail.


Module-2

Fundamentals of Machine Learning: Describing structural patterns, Machine Learning, Data Mining, Simple Examples, Fielded Examples, Machine Learning and statistics, Generalization as a search, Data mining and ethics.Data preprocessing using Weka, Handling high dimensional data through feature reduction in Weka.



Module-3

Machine Learning Tasks:Decision Tables, Decision Trees, Classification rules, Association rules, Rules with exceptions, Rules involving relations, Trees for numeric prediction, Instancebased representation, Clusters.Building soybean classification model using decision trees, generating association rules on weather data using Weka, Exploring Classification and Clustering techniques using scikit-learn or Weka.



Module-4

Nature-inspired techniques in AI:Inspiration from brain, Perceptron, Artificial Neural Net, Unsupervised Learning, Genetic Algorithms. Weather Prediction through Neural Networks using Weka, Perform data labelling for various images using Supervisely.



Module-5

Deep Learning: Basics of Deep Learning, Medical Image Analysis using Tensor Flow or Supervisely. Present and Future trends: The social effects of AI, A World with Robots, AI and Art, The Future, Integration, Artificial agents.


Suggested Learning Resources:

Text Book:

1. BlayWhitby, Artificial Intelligence: A Beginners Guide, Second Edition, One World Publisher, 2008.

2. Ian H. Witten, Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufman Publishers, 3rd Edition, 2011.


Reference Books:

1. AurĂ©lienGĂ©ron,Hands on Machine Learning with Scikit-Learn and TensorFlow [Concepts, Tools, and Techniques to Build Intelligent Systems], Published by O’Reilly Media,2017

2. Elaine Rich, Kevin Knight and Shivashankar B. Nair, Artificial Intelligence,TMH Education Pvt. Ltd., 2008.

3. Dan W. Patterson, Introduction to Artificial Intelligence and Expert Systems, Pearson.


Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 Course seminar

 Term projects

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