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Last Updated on Friday, 12 April 2024 12:05
 

ME644

MACHINE LEARNING FOR ENGINEERS

Credits:

  

 

3-0-0-9
 

Course contents:


Mathematical preliminaries, python programming, simple/multiple linear regression, nonlinear regression, logistic regression, k-nearest neighbours, perceptrons, random forest, naïve Bayes, support vector machines, artificial neural network, clustering, dimensionality reduction

Lecturewise Breakup (Based on 50 min per lecture)


Sl. No.TopicContentsLecture
1. Introduction Various learning paradigms, definitions, examples 1
2. Programming Programming in python, libraries: scientific computing, machine learning, plotting 2
3. Mathematics for machine learning Linear algebra and vector calculus: Vector space, vector-matrix operations, norm, eigenvalue and eigenvectors, matrix decompositions, differential calculus of vectors 3
Optimization: gradient-based techniques, metaheuristic techniques, numerical implementation 3
Statistics and probability: Probability distributions, hypotheses testing, Bayes’ theorem 3
4. Supervised learning Linear/nonlinear regression, overfitting, regularization, logistic regression, naive Bayes, k-NN, decision tree, random forest, maximum likelihood, support vector machine, applications in mechanical engineering 15
5. Unsupervised learning Singular value decomposition, principal component analysis, clustering, applications in mechanical engineering 8
6. Artificial neural network Single- and multi-layer networks, activation, backpropagation, stochastic gradient descent, physics-informed neural network, applications in mechanical engineering 5
Total 40

References:

  1. Machine Learning for Engineers, R. G. McClarren, Springer

  2. A First Course in Machine Learning, S. Rogera, M. Girolami, CRC Press

  3. Machine Learning, Z-H. Zhou, Springer

  4. An Introduction to Statistical Learning, G. James et al., Springer

  5. Data-Driven Science and Engineering, S. L. Brunton, J. L. Kutz, Cambridge Uni. press

  6. Probabilistic Machine Learning for Civil Engineers, J-A. Goulet, MIT Press

  7. Machine Learning Refined, 2nd ed., J. Watt et al., Cambridge University press

  8. Machine Learning, A. Lindholm et al., Cambridge University press