Course Outcome
PO1
PO2
PO3
PO4
PO5
PO6
PO7
PO8
PO9
PO10
PO11
PO12
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
CO1 CO2 CO3 CO4 CO5 CO6 Average
3 3 3 3 3 3 3
3 3 3 3 1 1 2
3 3 3 3 3 3
3 3 1
2 2 1
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1 1
-
-
-
-
-
L T P C MACHINE LEARNING AND DATA ANALYTICS FOR MECHANICAL ENGINEERING 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
19CS2011
COMPUTER SCIENCE AND ENGINEERING (2020)
