Machine Learning

The course focuses mainly on the applied aspects of machine learning with special emphasis on neural networks and deep learning. The course provides an introduction to machine learning and an overview of neural networks. The perceptron as the basic element for linear separability and its limitations in classification are discussed. Students then study the different activation functions and the sigmoid perceptron so that they can solve non linear classification problems. Different types of machine learning paradigms such as supervised, unsupervised, and reinforcement learning are covered. Feed forward neural networks and the back propagation algorithm are presented. The course also covers recurrent neural networks. Finally, deep learning is discussed with emphasis on the basic principles and different types of neural networks for deep learning.