Machine Learning for Core Engineering Disciplines

By Prof. Ananth Govind Rajan | IISc Bangalore

Learners enrolled: 1847 | Exam registration: 30

ABOUT THE COURSE:

This course will introduce students and practitioners of core engineering disciplines to the fundamentals and applications of machine learning. The course will cover the following material: introduction to data science and machine learning, examples of supervised/unsupervised/reinforcement learning, motivation for machine learning with examples derived from various core engineering disciplines; introduction to Python, scientific computing packages (NumPy, SciPy, Matplotlib), and simple ML packages (Scikit-learn, Pytorch); linear and nonlinear regression, confidence intervals and goodness of fit, loss functions, gradient descent algorithm, overfitting/underfitting, regression/classification learning; clustering algorithms, singular-value decomposition, principal component analysis, and nonlinear dimensionality reduction; decision trees and ensemble methods, boosting and bagging techniques, random forests, gradient-boosted machine learning, support vector machines, Gaussian process regression; hyperparameter tuning and cross validation; introduction to neural networks and deep learning; feed-forward, convolutional, and recurrent neural networks

INTENDED AUDIENCE: Core Engineering Disciplines

INDUSTRY SUPPORT: Several companies working in core engineering disciplines

Summary

Course Status :	Upcoming
Course Type :	Elective
Language for course content :	English
Duration :	12 weeks
Category :	Computational Engineering Multidisciplinary
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	21 Jul 2025
End Date :	10 Oct 2025
Enrollment Ends :	28 Jul 2025
Exam Registration Ends :	15 Aug 2025
Exam Date :	02 Nov 2025 IST
NCrF Level	4.5 — 8.0

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction to data science and machine learning; differences between supervised, unsupervised, and reinforcement learning; introduction to probability and statistics; sample and population properties

Week 2: Covariance and correlation matrix; Linear regression; model parametrization and fitting; coefficient of determination; confidence intervals and hypothesis testing

Week 3: Overfitting and underfitting; bias-variance tradeoff; dealing with overfitting via regularization (ridge regression and LASSO); logistic regression; softmax regression

Week 4: Basics of Python; loss functions including mean-squared error, mean absolute error, mean-squared logarithmic error, cross-entropy loss, Tweedie loss, and mean absolute percentage error; statistical basis of some loss functions

Week 5: Training of ML models; gradient descent algorithm; stochastic gradient descent; variants such as momentum, AdaGrad, Adam, and RMSprop

Week 6: k-fold cross validation, leave-one-out cross validation, and hyperparameter tuning via grid search, randomized search, and Bayesian approaches; implementation of regression models in Python

Week 7: Unsupervised learning; singular value decomposition; principal component analysis; clustering algorithms including k-means and density-based clustering

Week 8: Nonlinear dimensionality reduction via uniform manifold approximation and projection, t-distributed stochastic neighbour embedding, and autoencoders; implementation of unsupervised learning in Python; decision trees and their working; recursive binary splitting

Week 9: Regression and classification via decision trees; ensembling of decision trees; bagging and boosting; random forests; adaptive boosting; gradient boosting; introduction to support vector machines (SVM)

Week 10: SVM for classification and regression; Gaussian process regression (GPR); implementation of decision trees, SVM, and GPR in Python

Week 11: Introduction to neural networks; working of feed-forward and convolutional neural networks

Week 12: Recurrent neural networks; implementation of neural networks in Python

Books and references

Christopher M. Bishop, “Pattern Recognition and Machine Learning,” Springer
Trevor Hastie, Robert Tibshirani, and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer
Raghunathan Rengaswamy and Resmi Suresh, “Data Science for Engineers,” CRC Press

Instructor bio

Prof. Ananth Govind Rajan

IISc Bangalore

Dr. Ananth Govind Rajan is an Assistant Professor and Infosys Young Investigator in the Department of Chemical Engineering at the Indian Institute of Science (IISc), Bangalore. Dr. Govind Rajan received his B.Tech. from the Indian Institute of Technology (IIT) Delhi in 2013 and his Masters and Ph.D. in Chemical Engineering in 2015 and 2019, respectively, from the Massachusetts Institute of Technology (MIT). Subsequently, he conducted postdoctoral research at Princeton University, before joining IISc in 2020. Dr. Govind Rajan’s research interests lie in the modeling and simulation of nanomaterials, including their synthesis and applications for clean energy and water technologies. His group focuses on combining quantum-mechanical and molecular simulations with machine learning for modeling materials for electrochemical water splitting, catalytic carbon dioxide reduction to chemicals, and membrane separations. He is an Associate of the Indian National Academy of Engineering and Indian Academy of Sciences, and a recipient of the Amar-Dye Chem Award of the Indian Institute of Chemical Engineers. He has also received other honours such as the 2023 Class of Influential Researchers from American Chemical Society’s Industrial and Engineering Chemistry Research and the Graduates of the Last Decade Award from IIT Delhi.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.

The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).

Date and Time of Exams: November 02, 2025 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.

Registration url: Announcements will be made when the registration form is open for registrations.

The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.

Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

Please note that assignments encompass all types (including quizzes, programming tasks, and essay submissions) available in the specific week.

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IISc Bangalore .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team