select IOT platform for an application

Module 3: Unsupervised Learning with Clustering (8hrs)

Hierarchical Clustering -K-means Algorithm -Spectral Clustering -Affinity Propagation -Probabilistic Clustering

Module 4: Classification in Supervised Machine Learning (9 hrs)

Model Performance Evaluation - Feature Subset Selection - K-nearest Neighbors - Classification Tree - Rule Induction - Artificial Neural Networks - Support Vector Machines - Logistic Regression Bayesian Network

Module 5: Component Level Case Study (8 hrs)

Introduction - Ball bearing Prognostics - Feature Extraction from Vibration Signals - Hidden Markov Model-based RUL estimation - Results and Discussion

Module 6: Production-Level Case Study: Automated Visual Inspection of Laser Processing (8 hrs)

Introduction - Laser Surface Heat Treatment - Anomaly Detection-based AVI system - Results and Discussion

Text Books:

1. Pedro Larrinaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Esteban PuertoSantana, Concha Bielza, “Industrial Applications of Machine Learning”, First Edition, CRC Press, 2018. ISBN 9780815356226 - CAT# K346412. Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. 2. Guido Reiman, “Quick Guide Machine Learning in Mechanical and Plant Engineering”, VDMA Software and Digitalization, Germany, 2018. https://sud.vdma.org › 47bd499f-087d4650-0af6-a569d7825b0d

Reference Books:

1. Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition, Prentice Hall, 2010. 2. Elaine Rich, Kevin Knight, Shivashankar B. Nair, “Artificial Intelligence”, Third Edition, McGraw-Hill, 2009. ISBN -13: 973-0-07-008770-5. ISBN-10:0-07-008770-9.

Course Articulation Matrix

Course Outcome PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 CO1 3 3 - - - - - - - - - CO2 3 3 3 - 2 - - - - - - CO3 3 3 3 - 2 - - - - - - CO4 3 3 3 - - - - - - - - CO5 3 1 3 3 - - 1 - - - - CO6 3 1 3 3 - - 1 - - - - Average 3 2 3 1 1

19CS2011 MACHINE LEARNING AND DATA ANALYTICS FOR MECHANICAL ENGINEERING L T P C 3 0 0 3

Course Objectives:

Enable the student to 1. understand the human learning aspects and primitives in learning process by computer. 2. analyze the nature of problems solved with machine learning techniques. 3. design and implement suitable machine learning and data analytics techniques for a given application.

Course Outcomes:

The student will be able to 1. describe the concepts, mathematical background, applicability, limitations of existing machine learning techniques. 2. identify the performance evaluation criteria of the model developed