JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives

The main objective of this course is to introduce the concepts of Artificial

intelligence to students and enable them to apply the concepts in various applications of manufacturing engineering.

Course Outcomes

After completion of this course the student will be able to

• Understand concepts of AI and use various problem-solving methods
• Understand and describe ANN architecture and apply BPNN.
• Demonstrate image processing using ANN
• Understand and apply various supervised and un supervised learning methods.
• Understand and apply reinforcement learning and ensemble learning techniques

UNIT - I

Introduction to Artificial Intelligence and Problem Solving: Definition, History, Present state of Artificial Intelligence (AI), Phases of AI, Approaches to AI - Hard or Strong AI, Soft or Weak AI, Applied AI, Cognitive AI, and Applications domains focused on manufacturing-role of AI in Industrial Revolution 4.0, components, advantages, challenges. Problem solving methods- 1. Uninformed search includes Depth First Search (DFS), Breadth First Search (BFS), Uniform Cost Search (UCS), Depth Limited Search, Iterative Deepening Depth First Search (IDDFS) and bidirectional search. 2. Informed Search (heuristic search) includes greedy best first search, A* search, memory bounded heuristic search, learning to search better, Simple problems

UNIT - II

Neural Networks: Introduction to Perceptron and Neural Networks, Activation and Loss functions, Single Neuron of Human and Human Brain Modelling, ANN architecture-Input layer, Hidden layer and output layer, Types of Neural Networks- Single layer feed-forward network, Multilayer feed-forward network, Multi-Layer Perceptron (MLP), Recurrent networks or feedback ANN, Characteristics of Neural Networks, Simple problems on Back Propagation Algorithms to minimize the error

UNIT - III

Computer Vision: Introduction to Convolutional Neural Networks (CNNs), What is CNN, Common uses for CNN, CNN’s Basic Architecture- LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, Introduction to Images, representation, image extraction, segmentation, analysis, Simple demonstration on Image processing using ANN - Face detection, Finger print recognition etc

UNIT - IV

Supervised and Unsupervised Learning: Unsupervised learning- Definition, basic concepts, applications, K-means Clustering, hierarchical Clustering, Dimension Reduction-PCA, Simple Examples. Supervised Learning - Definition, basic concepts, applications, Linear Regression, Multiple Variable Linear Regression, Logistic Regression, Naive Bayes Classifiers, k-NN Classification, Support Vector Machine, Simple Examples.

UNIT - V

Reinforcement Learning and Ensemble Learning Techniques Reinforcement Learning: Reinforcement Learning (RL) Framework, Component of RL Framework, Types of RL Systems. Q-learning, Simple Examples.

Ensemble Learning Techniques: Introduction on ensemble methods, Decision Trees, Bagging, Random Forests, Boostin, Simple Examples

TEXT BOOK:

• 1. Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig, Prentice-Hall, Third Edition (2009)

REFERENCES:

• 1. Artificial Intelligence, Ela Kumar, Wiley, 2021
• 2. Artificial Intelligence: Concepts and Applications, Lavika Goel, Kindle Edition, Wiley, 2021
• 3. Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems, Edited by Ganesh M. Kakandikar and Dinesh G. Thakur, CRC press, First edition, 2021. M. TECH. I Year II Sem. (AMS)