Economics

Introduction to a “money view” of economic activity for modern times, building on the intellectual traditions of British central banking and American institutionalism. Part One explores the economics of payment systems and money markets. Part Two explores connections with foreign exchange and capital markets.

The impact of economic forces in our lives is sizable and pervasive. For this reason, it is impossible to understand the social and economic forces shaping our lives without a good understanding of basic economic principles. This course provides a quantitative and model-based introduction to such principles, and teaches how to apply them to make sense of a wide range of real world problems. Examples of applications include predicting the impact of technological changes in market prices, calculating the optimal gasoline tax, and measuring the value of new products.

This is Part Two of a two-part course. The last three or four decades have seen a remarkable evolution in the institutions that comprise the modern monetary system. The financial crisis of 2007-2009 is a wakeup call that we need a similar evolution in the analytical apparatus and theories that we use to understand that system.

Behavioral economics is the scientific study of decision making, and of the related topics of valuation, exchange, and interpersonal interactions. Drawing on methods from psychology, sociology, neurology, and economics, this remarkable discipline illuminates one of the most deeply fundamental activities of human existence: the decision process.

Why are we choosing to have fewer children, even as we put more time into raising each one? Why are we so often willing to follow the herd and the opinions of strangers when making important decisions, even when those decisions are deeply personal? Why do people bother to vote in elections even when they believe their vote can't possibly influence the outcome?

Economies are deeply complex systems. The global marketplace—even national, state and local economies—involve many economic actors behaving in rational and irrational ways, sustaining a dizzying array of interconnected activity. Because of the number of participants involved in this global exchange, the unpredictability of their actions, and the sheer variety of possible actions, some degree of economic uncertainty is inevitable.

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. –- with math & batteries included - using deep neural networks for RL tasks –- also known as "the hype train"

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.