 Hello, my name is Shudeshna Sharkar, I am from the Computer Science and Engineering Department of IIT, Kharagpur and I will be teaching a course on Introduction to Machine Learning. Machine Learning is an exciting field and it has also grown to be one of the most happening fields today. As it lies in the heart of many services, machine learning lies at the heart of systems that do email spam detection, handwriting recognition, self-driving cars, product recommendation, credit card fraud detection to name a few of the tasks. The growing abundance of digitally available data, the rise in computing power especially with cloud and the use of GPU for computing and the recent advancements in machine learning algorithms and tweaks to algorithms have made machine learning as one of the most promising fields today and it has given rise to the popularity and ubiquitous use of machine learning. In this course, we will cover a set of broad principles for machine learning and we will also talk about some of the very popular and promising methods in machine learning. The two simplest tasks in machine learning are supervised and unsupervised learning. In supervised learning, we are given a set of labeled data and the output class or the output value is given and you are required to come up with a function of the feature values to predict the output. In unsupervised learning which includes clustering, no output is given but machine learning comprises of finding structure in the data. In this course, we will look at some of the general principles of machine learning including how to evaluate machine learning algorithms, the concept of overfitting, regularization and many other we will give a brief exposure to computational learning theory and we will look at feature engineering and feature dimensionality reduction. Some of the popular machine learning algorithms like decision trees, support vector machines, basic Bayesian algorithms and neural networks will be covered. We will also talk briefly about deep neural networks which uses multi-layer neural networks in clever ways and have given rise to rapid advancements in the field of among other tasks feature speech recognition, machine translation, textual description of images and such things. We will look at clustering algorithms like K-Means and adaptive hierarchical clustering and also use EM, also cover EM based algorithms for unsupervised learning. Reinforcement learning is concerned with how agents can take actions in an environment so as to maximize some notion of cumulative reward. In reinforcement learning, the agent is not told the correct action at every step but it receives rewards and penalties at certain times. Reinforcement learning has applications in different areas like robotic control and game theoretic systems. So, this is a brief overview of the course. Thank you very much.