Machine Learning for Engineering and Science Applications


Prof. Balaji Srinivasan,

Prof. Ganapthy Krishnamurthi

IIT Madras

*Additional GST and optional Exam fee are applicable.

SKU: IIT Madras Category:


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.


Postgraduate students in all engineering and science disciplines. Mature senior undergraduate students may also attempt the course.


Familiarity with Multivariable Calculus, Linear Algebra, Probability, Statistics. Comfortable with basic programming.


Should be of interest to companies trying to employ engineers familiar with Machine Learning


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.

Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.


Course Details

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


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


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