Click here for this course related my assignments and sample programs.
 Mathematical Basics 1 – Introduction to Machine Learning, Linear Algebra
 Mathematical Basics 2 – Probability
 Computational Basics – Numerical computation and optimization, Introduction to Machine Learning packages
 Linear and Logistic Regression – Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications
 Neural Networks – Multilayer Perceptron, Backpropagation, Applications
 Convolutional Neural Networks 1 – CNN Operations, CNN architectures
 Convolutional Neural Networks 2 – Training, Transfer Learning, Applications
 Recurrent Neural Networks ¬– RNN, LSTM, GRU, Applications
 Classical Techniques 1 – Bayesian Regression, Binary Trees, Random Forests, SVM, Naïve Bayes, Applications
 Classical Techniques 2 – kMeans, kNN, GMM, Expectation Maximization, Applications
 Advanced Techniques 1 – Structured Probabilistic Models, Monte Carlo Methods
 Advanced Techniques 2 – Autoencoders, Generative Adversarial Networks
Recommended Books
Some books reccommended by NPTEL for this course:

Deep Learning, Goodfellow et al, MIT Press, 2017
The online version of the book available for free: deeplearningbook.org 
Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2009
The PDF version of the book available for free: PRML 
Deep Learning with Python, François Chollet, Manning Publications 2017
Not free. See the topics covered in the book in publisher’s page 
References to research papers will be provided through the course.
Research Papers
 Fully Convolutional Networks for Semantic Segmentation. Jonathan Long, Evan Shelhamer, Trevor Darrell
 Understanding the difficulty of training deep feedforward neural networks. Xavier Glorot Yoshua Bengio
 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Sergey Ioffe, Christian Szegedy