Machine Learning for Engineering and Science Applications

By Prof. Balaji Srinivasan and Prof. Ganapathy   |   IIT Madras
Learners enrolled: 10361
Recent applications of machine learning have exploded due to cheaply available computational resources as well as wide availability of data. Machine Learning (ML) techniques provides a set of tools that can automatically detect patterns in data which can then be utilized for predictions and for developing models. Developments in ML algorithms and computational capabilities have now made it possible to scale engineering analysis, decision making and design rapidly. This, however, requires an engineer to understand the limits and applicability of the appropriate ML algorithms. This course aims to provide a broad overview of modern algorithms in ML, so that engineers may apply these judiciously. Towards this end, the course will focus on broad heuristics governing basic ML algorithms in the context of specific engineering applications. Matlab will be used in this course but students will also be trained to implement these methods utilizing open source packages such as TensorFlow.

INTENDED AUDIENCE: Postgraduate students in all engineering and science disciplines. Mature senior undergraduate students may also attempt the course. 
PREREQUISITES: Familiarity with Multivariable Calculus, Linear Algebra, Probability, Statistics. Comfortable with basic programming.
INDUSTRY SUPPORT:   Should be of interest to companies trying to employ engineers familiar with Machine Learning

Thanks to the support from Math Works, enrolled students have access to MATLAB for the duration of the course.
Course Status : Completed
Course Type : Elective
Duration : 12 weeks
Category :
  • Computer Science and Engineering
Credit Points : 3
Level : Undergraduate
Start Date : 29 Jul 2019
End Date : 18 Oct 2019
Exam Date : 17 Nov 2019 IST

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

Page Visits

Course layout

Week 1:   Mathematical Basics 1 – Introduction to Machine Learning, Linear Algebra
Week 2:   Mathematical Basics 2 - Probability
Week 3:   Computational Basics – Numerical computation and optimization, Introduction to Machine learning packages
Week 4:   Linear and Logistic Regression – Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications
Week 5:   Neural Networks – Multilayer Perceptron, Backpropagation, Applications
Week 6:   Convolutional Neural Networks 1 – CNN Operations, CNN architectures
Week 7:   Convolutional Neural Networks 2 – Training, Transfer Learning, Applications
Week 8:   Recurrent Neural Networks RNN, LSTM, GRU, Applications
Week 9:   Classical Techniques 1 – Bayesian Regression, Binary Trees, Random Forests, SVM, Naïve Bayes, Applications
Week 10: Classical Techniques 2 – k-Means, kNN, GMM, Expectation Maximization, Applications
Week 11: Advanced Techniques 1 – Structured Probabilistic Models, Monte Carlo Methods
Week 12: Advanced Techniques 2 – Autoencoders, Generative Adversarial Network

Thanks to the support from MathWorks, enrolled students have access to MATLAB for the duration of the course.

Books and references

Deep Learning, Goodfellow et al, MIT Press, 20172.
Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 20093.
References to research papers will be provided through the course.

Instructor bio

Dr Balaji Srinivasan is a faculty member in the Mechanical Engineering Department at IIT-Madras. His areas of research interest include Numerical Analysis, Computational Fluid Dynamics and applications of Machine Learning. 

Dr Ganapthy Krishnamurthi is a faculty member in the Engineering Design Department at IIT-Madras. His areas of research interest include Medical Image Analysis and Image Reconstruction.

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: 17 November 2019, 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.

  • 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

  • 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 IIT Madras. It will be e-verifiable at nptel.ac.in/noc.
  • Only the e-certificate will be made available. Hard copies are being discontinued from July 2019 semester and will not be dispatched

MHRD logo Swayam logo


Goto google play store