NPTEL Machine Learning for Engineering and Science Applications


Click here for this course related my assignments and sample programs.


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

Some books reccommended by NPTEL for this course:

  1. Deep Learning, Goodfellow et al, MIT Press, 2017
    The online version of the book available for free: deeplearningbook.org

  2. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2009
    The PDF version of the book available for free: PRML

  3. Deep Learning with Python, François Chollet, Manning Publications 2017
    Not free. See the topics covered in the book in publisher’s page

  4. References to research papers will be provided through the course.

Research Papers