 Major sports tournaments like the World Cup creates enthusiastic responses from the fans, and if one gets a chance to watch it live, it is a lifetime experience. But how to ensure that the really enthusiastic and motivated fans watch such matches like the World Cup final in the stadium? Enthusiasm is personal, and the tournament organizers do not know it. Similarly, scarce resource like the Spectrum needs to be efficiently and fairly allocated among the competing bidders. But since the desirability of the Spectrum by the bidders are not known to the government, how should the government design the auction to A, reveal the desirability and B, maximize the revenue for the sale? If you are interested in such problems, then this is the course for you. Perhaps you already got the hint, yes, the organizers of the tournament or the auction need to design mechanisms that incentivize the spectators or the service providers do what is desirable by the organizers. But are all desirable properties possible to achieve? We will discuss this and many more in this course. Welcome to this introductory course on Game Theory and Mechanism Design. My name is Subhrabanath. I am an assistant professor at the Computer Science Department, IIT Bombay. In the next 12 weeks, we will be learning several topics in the course that deals with modeling, analyzing and designing social algorithms that we are going to call Mechanisms. So this course has two complementary parts, Game Theory and Mechanism Design. A game is defined as an interaction between agents who always want to optimize their own objectives. In Game Theory, we provide predictive guarantees about the outcomes of a given game. In Mechanism Design, we flip the question and try to design protocols or algorithms that we are going to call Mechanisms and we set the rules of the game. The goal is to ensure that self-interested players pick actions such that the objective of the Mechanism Designer is fulfilled. Naturally, this is a prescriptive approach. In this course, we will be equipped with a general purpose tool to analyze strategic behavior in multi-agent environments. We will mathematically model strategic agent interaction. We will design protocols or mechanisms that satisfy desirable economic and computational properties. All these analyses and guarantees have many applications in multi-agent environments like sponsored search advertisements, crowd sourcing, social networks, internet-based trade and many more. But to make the best use of this course, you need to have some background. You should be familiar with formal mathematical reasoning. You need to know the probability theory in quite some detail. Some basic amount of calculus, few basics of computational complexity and some familiarity with computer programming, though the course can be followed even without programming. The course will run for 12 weeks and each week there will be a short assignment that has 5% weightage towards the final grade. At the end of the course, we will have one final exam that has 40% weightage. The content will be entirely online. Weekly videos and supporting notes or reading materials will be posted on the course webpage along with weekly assignment. We will meet at the end of every week to have Q&A on the topics discussed in the week. This will be a real-time session. You are supposed to watch the videos carefully and study the lecture materials before each Q&A session. In addition, there will be teaching staff who will be responding to your offline questions on some platform. I hope that this course becomes useful to you. I look forward to your participation in the course. Happy learning!