Machine Learning and Deep learning - Fundamentals and Applications

ABOUT THE COURSE: In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. We will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. On completion of the course students will acquire the knowledge of applying Machine and Deep Learning techniques to solve various real-life problems. INTENDED AUDIENCE: UG, PG and PhD students and industry professionals who want to work in Machine and Deep Learning. PREREQUISITES: Knowledge of Linear Algebra, Probability and Random Process, PDE will be helpful. INDUSTRY SUPPORT: This is a very important course for industry professionals.