Mechanical Engineering

Indian Institute of Technology Kanpur

ME644

ME644
Machine Learning For Engineers
Credits:
 3-0-0-9
Course Content:

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