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302049: Artificial Intelligence & Machine Learning

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Teaching Scheme Credits Examination Scheme
Theory 3Hrs./Week Theory 3 In-Semester 30 Marks
Practical 2 Hrs./Week Practical 1 End-Semester 70 Marks
Oral 25 Marks
Prerequisites: Linear Algebra, Probability, Statistics, Logical Reasoning.
Course Objectives:
1. ACQUAINT with fundamentals of artificial intelligence and machine learning.
2. LEARN feature extraction and selection techniques for processing data set.
3. UNDERSTAND basic algorithms used in classification and regression problems.
4. OUTLINE steps involved in development of machine learning model.
5. FAMILIARIZE with concepts of reinforced and deep learning.
6. IMPLEMENT AND ANALYZE machine learning model in mechanical engineering
problems.
Course Outcomes:
On completion of the course, learner will be able to
CO1. DEMONSTRATE fundamentals of artificial intelligence and machine learning.
CO2. APPLY feature extraction and selection techniques.
CO3. APPLY machine learning algorithms for classification and regression problems.
CO4. DEVISE AND DEVELOP a machine learning model using various steps.
CO5. EXPLAIN concepts of reinforced and deep learning.
CO6. SIMULATE machine learning model in mechanical engineering problems.
Course Contents
Unit 1 Introduction to AI & ML 06 Hrs.
History of AI, Comparison of AI with Data Science, Need of AI in Mechanical Engineering,
Introduction to Machine Learning. Basics: Reasoning, problem solving, Knowledge representation,
Planning, Learning, Perception, Motion and manipulation.
Approaches to AI: Cybernetics and brain simulation, Symbolic, Sub-symbolic, Statistical.
Approaches to ML: Supervised learning, Unsupervised learning, Reinforcement learning.
Unit 2 Feature Extraction and Selection 08 Hrs.
Feature extraction: Statistical features, Principal Component Analysis.
Feature selection: Ranking, Decision tree – Entropy reduction and information gain, Exhaustive,
best first, Greedy forward & backward, Applications of feature extraction and selection algorithms
in Mechanical Engineering.
Unit 3 Classification & Regression 08 Hrs.
Classification: Decision tree, Random forest, Naive Bayes, Support vector machine.
Regression: Logistic Regression, Support Vector Regression. Regression trees: Decision tree,
random forest, K-Means, K-Nearest Neighbor (KNN). Applications of classification and regression
algorithms in Mechanical Engineering.

Unit 4 Development of ML Model 07 Hrs.
Problem identification: classification, clustering, regression, ranking. Steps in ML modeling, Data
Collection, Data pre-processing, Model Selection, Model training (Training, Testing, K-fold Cross
Validation), Model evaluation (understanding and interpretation of confusion matrix, Accuracy,
Precision, Recall, True positive, false positive etc.), Hyper parameter Tuning, Predictions.
Unit 5 Reinforced and Deep Learning 08 Hrs.
Characteristics of reinforced learning; Algorithms: Value Based, Policy Based, Model Based;
Positive vs Negative Reinforced Learning; Models: Markov Decision Process, Q Learning.
Characteristics of Deep Learning, Artificial Neural Network, Convolution Neural Network.
Application of Reinforced and Deep Learning in Mechanical Engineering.
Unit 6 Applications 08 Hrs.
Human Machine Interaction, Predictive Maintenance and Health Management, Fault Detection,
Dynamic System Order Reduction, Image based part classification, Process Optimization, Material
Inspection, Tuning of control algorithms.